1504 lines
67 KiB
JSON
1504 lines
67 KiB
JSON
{
|
||
"description": "A financial research agent that takes in a query, plan the steps, search the web, and return a detailed report",
|
||
"usecases": ["Finance & Accounting"],
|
||
"nodes": [
|
||
{
|
||
"id": "startAgentflow_0",
|
||
"type": "agentFlow",
|
||
"position": {
|
||
"x": -234.94624728418063,
|
||
"y": 84.92919739582129
|
||
},
|
||
"data": {
|
||
"id": "startAgentflow_0",
|
||
"label": "Start",
|
||
"version": 1,
|
||
"name": "startAgentflow",
|
||
"type": "Start",
|
||
"color": "#7EE787",
|
||
"hideInput": true,
|
||
"baseClasses": ["Start"],
|
||
"category": "Agent Flows",
|
||
"description": "Starting point of the agentflow",
|
||
"inputParams": [
|
||
{
|
||
"label": "Input Type",
|
||
"name": "startInputType",
|
||
"type": "options",
|
||
"options": [
|
||
{
|
||
"label": "Chat Input",
|
||
"name": "chatInput",
|
||
"description": "Start the conversation with chat input"
|
||
},
|
||
{
|
||
"label": "Form Input",
|
||
"name": "formInput",
|
||
"description": "Start the workflow with form inputs"
|
||
}
|
||
],
|
||
"default": "chatInput",
|
||
"id": "startAgentflow_0-input-startInputType-options",
|
||
"display": true
|
||
},
|
||
{
|
||
"label": "Form Title",
|
||
"name": "formTitle",
|
||
"type": "string",
|
||
"placeholder": "Please Fill Out The Form",
|
||
"show": {
|
||
"startInputType": "formInput"
|
||
},
|
||
"id": "startAgentflow_0-input-formTitle-string",
|
||
"display": true
|
||
},
|
||
{
|
||
"label": "Form Description",
|
||
"name": "formDescription",
|
||
"type": "string",
|
||
"placeholder": "Complete all fields below to continue",
|
||
"show": {
|
||
"startInputType": "formInput"
|
||
},
|
||
"id": "startAgentflow_0-input-formDescription-string",
|
||
"display": true
|
||
},
|
||
{
|
||
"label": "Form Input Types",
|
||
"name": "formInputTypes",
|
||
"description": "Specify the type of form input",
|
||
"type": "array",
|
||
"show": {
|
||
"startInputType": "formInput"
|
||
},
|
||
"array": [
|
||
{
|
||
"label": "Type",
|
||
"name": "type",
|
||
"type": "options",
|
||
"options": [
|
||
{
|
||
"label": "String",
|
||
"name": "string"
|
||
},
|
||
{
|
||
"label": "Number",
|
||
"name": "number"
|
||
},
|
||
{
|
||
"label": "Boolean",
|
||
"name": "boolean"
|
||
},
|
||
{
|
||
"label": "Options",
|
||
"name": "options"
|
||
}
|
||
],
|
||
"default": "string"
|
||
},
|
||
{
|
||
"label": "Label",
|
||
"name": "label",
|
||
"type": "string",
|
||
"placeholder": "Label for the input"
|
||
},
|
||
{
|
||
"label": "Variable Name",
|
||
"name": "name",
|
||
"type": "string",
|
||
"placeholder": "Variable name for the input (must be camel case)",
|
||
"description": "Variable name must be camel case. For example: firstName, lastName, etc."
|
||
},
|
||
{
|
||
"label": "Add Options",
|
||
"name": "addOptions",
|
||
"type": "array",
|
||
"show": {
|
||
"formInputTypes[$index].type": "options"
|
||
},
|
||
"array": [
|
||
{
|
||
"label": "Option",
|
||
"name": "option",
|
||
"type": "string"
|
||
}
|
||
]
|
||
}
|
||
],
|
||
"id": "startAgentflow_0-input-formInputTypes-array",
|
||
"display": true
|
||
},
|
||
{
|
||
"label": "Ephemeral Memory",
|
||
"name": "startEphemeralMemory",
|
||
"type": "boolean",
|
||
"description": "Start fresh for every execution without past chat history",
|
||
"optional": true
|
||
},
|
||
{
|
||
"label": "Flow State",
|
||
"name": "startState",
|
||
"description": "Runtime state during the execution of the workflow",
|
||
"type": "array",
|
||
"optional": true,
|
||
"array": [
|
||
{
|
||
"label": "Key",
|
||
"name": "key",
|
||
"type": "string",
|
||
"placeholder": "Foo"
|
||
},
|
||
{
|
||
"label": "Value",
|
||
"name": "value",
|
||
"type": "string",
|
||
"placeholder": "Bar"
|
||
}
|
||
],
|
||
"id": "startAgentflow_0-input-startState-array",
|
||
"display": true
|
||
}
|
||
],
|
||
"inputAnchors": [],
|
||
"inputs": {
|
||
"startInputType": "formInput",
|
||
"formTitle": "Finanical Research",
|
||
"formDescription": "A financial research agent that takes in a query, and return a detailed report",
|
||
"formInputTypes": [
|
||
{
|
||
"type": "string",
|
||
"label": "Query",
|
||
"name": "query",
|
||
"addOptions": ""
|
||
}
|
||
],
|
||
"startState": [
|
||
{
|
||
"key": "search_key_reason",
|
||
"value": ""
|
||
}
|
||
]
|
||
},
|
||
"outputAnchors": [
|
||
{
|
||
"id": "startAgentflow_0-output-startAgentflow",
|
||
"label": "Start",
|
||
"name": "startAgentflow"
|
||
}
|
||
],
|
||
"outputs": {},
|
||
"selected": false
|
||
},
|
||
"width": 101,
|
||
"height": 65,
|
||
"selected": false,
|
||
"positionAbsolute": {
|
||
"x": -234.94624728418063,
|
||
"y": 84.92919739582129
|
||
},
|
||
"dragging": false
|
||
},
|
||
{
|
||
"id": "llmAgentflow_0",
|
||
"position": {
|
||
"x": -92.42002168895628,
|
||
"y": 81.69973969492588
|
||
},
|
||
"data": {
|
||
"id": "llmAgentflow_0",
|
||
"label": "Planner",
|
||
"version": 1,
|
||
"name": "llmAgentflow",
|
||
"type": "LLM",
|
||
"color": "#64B5F6",
|
||
"baseClasses": ["LLM"],
|
||
"category": "Agent Flows",
|
||
"description": "Large language models to analyze user-provided inputs and generate responses",
|
||
"inputParams": [
|
||
{
|
||
"label": "Model",
|
||
"name": "llmModel",
|
||
"type": "asyncOptions",
|
||
"loadMethod": "listModels",
|
||
"loadConfig": true,
|
||
"id": "llmAgentflow_0-input-llmModel-asyncOptions",
|
||
"display": true
|
||
},
|
||
{
|
||
"label": "Messages",
|
||
"name": "llmMessages",
|
||
"type": "array",
|
||
"optional": true,
|
||
"acceptVariable": true,
|
||
"array": [
|
||
{
|
||
"label": "Role",
|
||
"name": "role",
|
||
"type": "options",
|
||
"options": [
|
||
{
|
||
"label": "System",
|
||
"name": "system"
|
||
},
|
||
{
|
||
"label": "Assistant",
|
||
"name": "assistant"
|
||
},
|
||
{
|
||
"label": "Developer",
|
||
"name": "developer"
|
||
},
|
||
{
|
||
"label": "User",
|
||
"name": "user"
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"label": "Content",
|
||
"name": "content",
|
||
"type": "string",
|
||
"acceptVariable": true,
|
||
"generateInstruction": true,
|
||
"rows": 4
|
||
}
|
||
],
|
||
"id": "llmAgentflow_0-input-llmMessages-array",
|
||
"display": true
|
||
},
|
||
{
|
||
"label": "Enable Memory",
|
||
"name": "llmEnableMemory",
|
||
"type": "boolean",
|
||
"description": "Enable memory for the conversation thread",
|
||
"default": true,
|
||
"optional": true,
|
||
"id": "llmAgentflow_0-input-llmEnableMemory-boolean",
|
||
"display": true
|
||
},
|
||
{
|
||
"label": "Memory Type",
|
||
"name": "llmMemoryType",
|
||
"type": "options",
|
||
"options": [
|
||
{
|
||
"label": "All Messages",
|
||
"name": "allMessages",
|
||
"description": "Retrieve all messages from the conversation"
|
||
},
|
||
{
|
||
"label": "Window Size",
|
||
"name": "windowSize",
|
||
"description": "Uses a fixed window size to surface the last N messages"
|
||
},
|
||
{
|
||
"label": "Conversation Summary",
|
||
"name": "conversationSummary",
|
||
"description": "Summarizes the whole conversation"
|
||
},
|
||
{
|
||
"label": "Conversation Summary Buffer",
|
||
"name": "conversationSummaryBuffer",
|
||
"description": "Summarize conversations once token limit is reached. Default to 2000"
|
||
}
|
||
],
|
||
"optional": true,
|
||
"default": "allMessages",
|
||
"show": {
|
||
"llmEnableMemory": true
|
||
},
|
||
"id": "llmAgentflow_0-input-llmMemoryType-options",
|
||
"display": true
|
||
},
|
||
{
|
||
"label": "Window Size",
|
||
"name": "llmMemoryWindowSize",
|
||
"type": "number",
|
||
"default": "20",
|
||
"description": "Uses a fixed window size to surface the last N messages",
|
||
"show": {
|
||
"llmMemoryType": "windowSize"
|
||
},
|
||
"id": "llmAgentflow_0-input-llmMemoryWindowSize-number",
|
||
"display": false
|
||
},
|
||
{
|
||
"label": "Max Token Limit",
|
||
"name": "llmMemoryMaxTokenLimit",
|
||
"type": "number",
|
||
"default": "2000",
|
||
"description": "Summarize conversations once token limit is reached. Default to 2000",
|
||
"show": {
|
||
"llmMemoryType": "conversationSummaryBuffer"
|
||
},
|
||
"id": "llmAgentflow_0-input-llmMemoryMaxTokenLimit-number",
|
||
"display": false
|
||
},
|
||
{
|
||
"label": "Input Message",
|
||
"name": "llmUserMessage",
|
||
"type": "string",
|
||
"description": "Add an input message as user message at the end of the conversation",
|
||
"rows": 4,
|
||
"optional": true,
|
||
"acceptVariable": true,
|
||
"show": {
|
||
"llmEnableMemory": true
|
||
},
|
||
"id": "llmAgentflow_0-input-llmUserMessage-string",
|
||
"display": true
|
||
},
|
||
{
|
||
"label": "Return Response As",
|
||
"name": "llmReturnResponseAs",
|
||
"type": "options",
|
||
"options": [
|
||
{
|
||
"label": "User Message",
|
||
"name": "userMessage"
|
||
},
|
||
{
|
||
"label": "Assistant Message",
|
||
"name": "assistantMessage"
|
||
}
|
||
],
|
||
"default": "userMessage",
|
||
"id": "llmAgentflow_0-input-llmReturnResponseAs-options",
|
||
"display": true
|
||
},
|
||
{
|
||
"label": "JSON Structured Output",
|
||
"name": "llmStructuredOutput",
|
||
"description": "Instruct the LLM to give output in a JSON structured schema",
|
||
"type": "array",
|
||
"optional": true,
|
||
"acceptVariable": true,
|
||
"array": [
|
||
{
|
||
"label": "Key",
|
||
"name": "key",
|
||
"type": "string"
|
||
},
|
||
{
|
||
"label": "Type",
|
||
"name": "type",
|
||
"type": "options",
|
||
"options": [
|
||
{
|
||
"label": "String",
|
||
"name": "string"
|
||
},
|
||
{
|
||
"label": "String Array",
|
||
"name": "stringArray"
|
||
},
|
||
{
|
||
"label": "Number",
|
||
"name": "number"
|
||
},
|
||
{
|
||
"label": "Boolean",
|
||
"name": "boolean"
|
||
},
|
||
{
|
||
"label": "Enum",
|
||
"name": "enum"
|
||
},
|
||
{
|
||
"label": "JSON Array",
|
||
"name": "jsonArray"
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"label": "Enum Values",
|
||
"name": "enumValues",
|
||
"type": "string",
|
||
"placeholder": "value1, value2, value3",
|
||
"description": "Enum values. Separated by comma",
|
||
"optional": true,
|
||
"show": {
|
||
"llmStructuredOutput[$index].type": "enum"
|
||
}
|
||
},
|
||
{
|
||
"label": "JSON Schema",
|
||
"name": "jsonSchema",
|
||
"type": "code",
|
||
"placeholder": "{\n \"answer\": {\n \"type\": \"string\",\n \"description\": \"Value of the answer\"\n },\n \"reason\": {\n \"type\": \"string\",\n \"description\": \"Reason for the answer\"\n },\n \"optional\": {\n \"type\": \"boolean\"\n },\n \"count\": {\n \"type\": \"number\"\n },\n \"children\": {\n \"type\": \"array\",\n \"items\": {\n \"type\": \"object\",\n \"properties\": {\n \"value\": {\n \"type\": \"string\",\n \"description\": \"Value of the children's answer\"\n }\n }\n }\n }\n}",
|
||
"description": "JSON schema for the structured output",
|
||
"optional": true,
|
||
"show": {
|
||
"llmStructuredOutput[$index].type": "jsonArray"
|
||
}
|
||
},
|
||
{
|
||
"label": "Description",
|
||
"name": "description",
|
||
"type": "string",
|
||
"placeholder": "Description of the key"
|
||
}
|
||
],
|
||
"id": "llmAgentflow_0-input-llmStructuredOutput-array",
|
||
"display": true
|
||
},
|
||
{
|
||
"label": "Update Flow State",
|
||
"name": "llmUpdateState",
|
||
"description": "Update runtime state during the execution of the workflow",
|
||
"type": "array",
|
||
"optional": true,
|
||
"acceptVariable": true,
|
||
"array": [
|
||
{
|
||
"label": "Key",
|
||
"name": "key",
|
||
"type": "asyncOptions",
|
||
"loadMethod": "listRuntimeStateKeys",
|
||
"freeSolo": true
|
||
},
|
||
{
|
||
"label": "Value",
|
||
"name": "value",
|
||
"type": "string",
|
||
"acceptVariable": true,
|
||
"acceptNodeOutputAsVariable": true
|
||
}
|
||
],
|
||
"id": "llmAgentflow_0-input-llmUpdateState-array",
|
||
"display": true
|
||
}
|
||
],
|
||
"inputAnchors": [],
|
||
"inputs": {
|
||
"llmModel": "chatOpenAI",
|
||
"llmMessages": [
|
||
{
|
||
"role": "system",
|
||
"content": "<p>You are a financial research planner. Given a request for financial analysis, produce a set of web searches to gather the context needed. Aim for recent headlines, earnings calls or 10‑K snippets, analyst commentary, and industry background. Output between 1 and 2 search terms to query for.</p>"
|
||
},
|
||
{
|
||
"role": "user",
|
||
"content": "<p>Query:</p><p><span class=\"variable\" data-type=\"mention\" data-id=\"$form.query\" data-label=\"$form.query\">{{ $form.query }}</span></p>"
|
||
}
|
||
],
|
||
"llmEnableMemory": true,
|
||
"llmReturnResponseAs": "userMessage",
|
||
"llmStructuredOutput": [
|
||
{
|
||
"key": "searches",
|
||
"type": "jsonArray",
|
||
"enumValues": "",
|
||
"jsonSchema": "{\n \"query\": {\n \"type\": \"string\",\n \"description\": \"The search term to feed into a web (or file) search.\"\n },\n \"reason\": {\n \"type\": \"string\",\n \"description\": \"Your reasoning for why this search is relevant.\"\n }\n}",
|
||
"description": "A list of searches to perform"
|
||
}
|
||
],
|
||
"llmUpdateState": [
|
||
{
|
||
"key": "search_key_reason",
|
||
"value": "<p><span class=\"variable\" data-type=\"mention\" data-id=\"output.searches\" data-label=\"output.searches\">{{ output.searches }}</span> </p>"
|
||
}
|
||
],
|
||
"llmModelConfig": {
|
||
"cache": "",
|
||
"modelName": "gpt-4o-mini",
|
||
"temperature": 0.9,
|
||
"streaming": true,
|
||
"maxTokens": "",
|
||
"topP": "",
|
||
"frequencyPenalty": "",
|
||
"presencePenalty": "",
|
||
"timeout": "",
|
||
"strictToolCalling": "",
|
||
"stopSequence": "",
|
||
"basepath": "",
|
||
"proxyUrl": "",
|
||
"baseOptions": "",
|
||
"allowImageUploads": "",
|
||
"imageResolution": "low",
|
||
"reasoningEffort": "medium",
|
||
"llmModel": "chatOpenAI"
|
||
},
|
||
"llmUserMessage": "<p></p>"
|
||
},
|
||
"outputAnchors": [
|
||
{
|
||
"id": "llmAgentflow_0-output-llmAgentflow",
|
||
"label": "LLM",
|
||
"name": "llmAgentflow"
|
||
}
|
||
],
|
||
"outputs": {},
|
||
"selected": false
|
||
},
|
||
"type": "agentFlow",
|
||
"width": 168,
|
||
"height": 71,
|
||
"selected": false,
|
||
"positionAbsolute": {
|
||
"x": -92.42002168895628,
|
||
"y": 81.69973969492588
|
||
},
|
||
"dragging": false
|
||
},
|
||
{
|
||
"id": "iterationAgentflow_0",
|
||
"position": {
|
||
"x": 122.70987564816664,
|
||
"y": -7.337791594648152
|
||
},
|
||
"data": {
|
||
"id": "iterationAgentflow_0",
|
||
"label": "Iteration 0",
|
||
"version": 1,
|
||
"name": "iterationAgentflow",
|
||
"type": "Iteration",
|
||
"color": "#9C89B8",
|
||
"baseClasses": ["Iteration"],
|
||
"category": "Agent Flows",
|
||
"description": "Execute the nodes within the iteration block through N iterations",
|
||
"inputParams": [
|
||
{
|
||
"label": "Array Input",
|
||
"name": "iterationInput",
|
||
"type": "string",
|
||
"description": "The input array to iterate over",
|
||
"acceptVariable": true,
|
||
"rows": 4,
|
||
"id": "iterationAgentflow_0-input-iterationInput-string",
|
||
"display": true
|
||
}
|
||
],
|
||
"inputAnchors": [],
|
||
"inputs": {
|
||
"iterationInput": "<p><span class=\"variable\" data-type=\"mention\" data-id=\"$flow.state.search_key_reason\" data-label=\"$flow.state.search_key_reason\">{{ $flow.state.search_key_reason }}</span> </p>"
|
||
},
|
||
"outputAnchors": [
|
||
{
|
||
"id": "iterationAgentflow_0-output-iterationAgentflow",
|
||
"label": "Iteration",
|
||
"name": "iterationAgentflow"
|
||
}
|
||
],
|
||
"outputs": {},
|
||
"selected": false
|
||
},
|
||
"type": "iteration",
|
||
"width": 300,
|
||
"height": 250,
|
||
"selected": false,
|
||
"positionAbsolute": {
|
||
"x": 122.70987564816664,
|
||
"y": -7.337791594648152
|
||
},
|
||
"dragging": false
|
||
},
|
||
{
|
||
"id": "agentAgentflow_0",
|
||
"position": {
|
||
"x": 67.5,
|
||
"y": 80.5
|
||
},
|
||
"data": {
|
||
"id": "agentAgentflow_0",
|
||
"label": "Search Agent",
|
||
"version": 1,
|
||
"name": "agentAgentflow",
|
||
"type": "Agent",
|
||
"color": "#4DD0E1",
|
||
"baseClasses": ["Agent"],
|
||
"category": "Agent Flows",
|
||
"description": "Dynamically choose and utilize tools during runtime, enabling multi-step reasoning",
|
||
"inputParams": [
|
||
{
|
||
"label": "Model",
|
||
"name": "agentModel",
|
||
"type": "asyncOptions",
|
||
"loadMethod": "listModels",
|
||
"loadConfig": true,
|
||
"id": "agentAgentflow_0-input-agentModel-asyncOptions",
|
||
"display": true
|
||
},
|
||
{
|
||
"label": "Messages",
|
||
"name": "agentMessages",
|
||
"type": "array",
|
||
"optional": true,
|
||
"acceptVariable": true,
|
||
"array": [
|
||
{
|
||
"label": "Role",
|
||
"name": "role",
|
||
"type": "options",
|
||
"options": [
|
||
{
|
||
"label": "System",
|
||
"name": "system"
|
||
},
|
||
{
|
||
"label": "Assistant",
|
||
"name": "assistant"
|
||
},
|
||
{
|
||
"label": "Developer",
|
||
"name": "developer"
|
||
},
|
||
{
|
||
"label": "User",
|
||
"name": "user"
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"label": "Content",
|
||
"name": "content",
|
||
"type": "string",
|
||
"acceptVariable": true,
|
||
"generateInstruction": true,
|
||
"rows": 4
|
||
}
|
||
],
|
||
"id": "agentAgentflow_0-input-agentMessages-array",
|
||
"display": true
|
||
},
|
||
{
|
||
"label": "Tools",
|
||
"name": "agentTools",
|
||
"type": "array",
|
||
"optional": true,
|
||
"array": [
|
||
{
|
||
"label": "Tool",
|
||
"name": "agentSelectedTool",
|
||
"type": "asyncOptions",
|
||
"loadMethod": "listTools",
|
||
"loadConfig": true
|
||
},
|
||
{
|
||
"label": "Require Human Input",
|
||
"name": "agentSelectedToolRequiresHumanInput",
|
||
"type": "boolean",
|
||
"optional": true
|
||
}
|
||
],
|
||
"id": "agentAgentflow_0-input-agentTools-array",
|
||
"display": true
|
||
},
|
||
{
|
||
"label": "Knowledge (Document Stores)",
|
||
"name": "agentKnowledgeDocumentStores",
|
||
"type": "array",
|
||
"description": "Give your agent context about different document sources. Document stores must be upserted in advance.",
|
||
"array": [
|
||
{
|
||
"label": "Document Store",
|
||
"name": "documentStore",
|
||
"type": "asyncOptions",
|
||
"loadMethod": "listStores"
|
||
},
|
||
{
|
||
"label": "Describe Knowledge",
|
||
"name": "docStoreDescription",
|
||
"type": "string",
|
||
"generateDocStoreDescription": true,
|
||
"placeholder": "Describe what the knowledge base is about, this is useful for the AI to know when and how to search for correct information",
|
||
"rows": 4
|
||
},
|
||
{
|
||
"label": "Return Source Documents",
|
||
"name": "returnSourceDocuments",
|
||
"type": "boolean",
|
||
"optional": true
|
||
}
|
||
],
|
||
"optional": true,
|
||
"id": "agentAgentflow_0-input-agentKnowledgeDocumentStores-array",
|
||
"display": true
|
||
},
|
||
{
|
||
"label": "Knowledge (Vector Embeddings)",
|
||
"name": "agentKnowledgeVSEmbeddings",
|
||
"type": "array",
|
||
"description": "Give your agent context about different document sources from existing vector stores and embeddings",
|
||
"array": [
|
||
{
|
||
"label": "Vector Store",
|
||
"name": "vectorStore",
|
||
"type": "asyncOptions",
|
||
"loadMethod": "listVectorStores",
|
||
"loadConfig": true
|
||
},
|
||
{
|
||
"label": "Embedding Model",
|
||
"name": "embeddingModel",
|
||
"type": "asyncOptions",
|
||
"loadMethod": "listEmbeddings",
|
||
"loadConfig": true
|
||
},
|
||
{
|
||
"label": "Knowledge Name",
|
||
"name": "knowledgeName",
|
||
"type": "string",
|
||
"placeholder": "A short name for the knowledge base, this is useful for the AI to know when and how to search for correct information"
|
||
},
|
||
{
|
||
"label": "Describe Knowledge",
|
||
"name": "knowledgeDescription",
|
||
"type": "string",
|
||
"placeholder": "Describe what the knowledge base is about, this is useful for the AI to know when and how to search for correct information",
|
||
"rows": 4
|
||
},
|
||
{
|
||
"label": "Return Source Documents",
|
||
"name": "returnSourceDocuments",
|
||
"type": "boolean",
|
||
"optional": true
|
||
}
|
||
],
|
||
"optional": true,
|
||
"id": "agentAgentflow_0-input-agentKnowledgeVSEmbeddings-array",
|
||
"display": true
|
||
},
|
||
{
|
||
"label": "Enable Memory",
|
||
"name": "agentEnableMemory",
|
||
"type": "boolean",
|
||
"description": "Enable memory for the conversation thread",
|
||
"default": true,
|
||
"optional": true,
|
||
"id": "agentAgentflow_0-input-agentEnableMemory-boolean",
|
||
"display": true
|
||
},
|
||
{
|
||
"label": "Memory Type",
|
||
"name": "agentMemoryType",
|
||
"type": "options",
|
||
"options": [
|
||
{
|
||
"label": "All Messages",
|
||
"name": "allMessages",
|
||
"description": "Retrieve all messages from the conversation"
|
||
},
|
||
{
|
||
"label": "Window Size",
|
||
"name": "windowSize",
|
||
"description": "Uses a fixed window size to surface the last N messages"
|
||
},
|
||
{
|
||
"label": "Conversation Summary",
|
||
"name": "conversationSummary",
|
||
"description": "Summarizes the whole conversation"
|
||
},
|
||
{
|
||
"label": "Conversation Summary Buffer",
|
||
"name": "conversationSummaryBuffer",
|
||
"description": "Summarize conversations once token limit is reached. Default to 2000"
|
||
}
|
||
],
|
||
"optional": true,
|
||
"default": "allMessages",
|
||
"show": {
|
||
"agentEnableMemory": true
|
||
},
|
||
"id": "agentAgentflow_0-input-agentMemoryType-options",
|
||
"display": true
|
||
},
|
||
{
|
||
"label": "Window Size",
|
||
"name": "agentMemoryWindowSize",
|
||
"type": "number",
|
||
"default": "20",
|
||
"description": "Uses a fixed window size to surface the last N messages",
|
||
"show": {
|
||
"agentMemoryType": "windowSize"
|
||
},
|
||
"id": "agentAgentflow_0-input-agentMemoryWindowSize-number",
|
||
"display": false
|
||
},
|
||
{
|
||
"label": "Max Token Limit",
|
||
"name": "agentMemoryMaxTokenLimit",
|
||
"type": "number",
|
||
"default": "2000",
|
||
"description": "Summarize conversations once token limit is reached. Default to 2000",
|
||
"show": {
|
||
"agentMemoryType": "conversationSummaryBuffer"
|
||
},
|
||
"id": "agentAgentflow_0-input-agentMemoryMaxTokenLimit-number",
|
||
"display": false
|
||
},
|
||
{
|
||
"label": "Input Message",
|
||
"name": "agentUserMessage",
|
||
"type": "string",
|
||
"description": "Add an input message as user message at the end of the conversation",
|
||
"rows": 4,
|
||
"optional": true,
|
||
"acceptVariable": true,
|
||
"show": {
|
||
"agentEnableMemory": true
|
||
},
|
||
"id": "agentAgentflow_0-input-agentUserMessage-string",
|
||
"display": true
|
||
},
|
||
{
|
||
"label": "Return Response As",
|
||
"name": "agentReturnResponseAs",
|
||
"type": "options",
|
||
"options": [
|
||
{
|
||
"label": "User Message",
|
||
"name": "userMessage"
|
||
},
|
||
{
|
||
"label": "Assistant Message",
|
||
"name": "assistantMessage"
|
||
}
|
||
],
|
||
"default": "userMessage",
|
||
"id": "agentAgentflow_0-input-agentReturnResponseAs-options",
|
||
"display": true
|
||
},
|
||
{
|
||
"label": "Update Flow State",
|
||
"name": "agentUpdateState",
|
||
"description": "Update runtime state during the execution of the workflow",
|
||
"type": "array",
|
||
"optional": true,
|
||
"acceptVariable": true,
|
||
"array": [
|
||
{
|
||
"label": "Key",
|
||
"name": "key",
|
||
"type": "asyncOptions",
|
||
"loadMethod": "listRuntimeStateKeys",
|
||
"freeSolo": true
|
||
},
|
||
{
|
||
"label": "Value",
|
||
"name": "value",
|
||
"type": "string",
|
||
"acceptVariable": true,
|
||
"acceptNodeOutputAsVariable": true
|
||
}
|
||
],
|
||
"id": "agentAgentflow_0-input-agentUpdateState-array",
|
||
"display": true
|
||
}
|
||
],
|
||
"inputAnchors": [],
|
||
"inputs": {
|
||
"agentModel": "chatOpenAI",
|
||
"agentMessages": [
|
||
{
|
||
"role": "system",
|
||
"content": "<p>You are a research assistant specializing in financial topics. Given a search term, use web search to retrieve up‑to‑date context and produce a short summary of at most 300 words. Focus on key numbers, events, or quotes that will be useful to a financial analyst.</p>"
|
||
}
|
||
],
|
||
"agentTools": [
|
||
{
|
||
"agentSelectedTool": "googleCustomSearch",
|
||
"agentSelectedToolConfig": {
|
||
"agentSelectedTool": "googleCustomSearch"
|
||
}
|
||
}
|
||
],
|
||
"agentKnowledgeDocumentStores": "",
|
||
"agentEnableMemory": true,
|
||
"agentMemoryType": "allMessages",
|
||
"agentUserMessage": "<p>Search term: {{$iteration.query}}</p><p>Reason: {{$iteration.reason}}</p>",
|
||
"agentReturnResponseAs": "userMessage",
|
||
"agentUpdateState": "",
|
||
"agentModelConfig": {
|
||
"cache": "",
|
||
"modelName": "gpt-4o-mini",
|
||
"temperature": 0.9,
|
||
"streaming": true,
|
||
"maxTokens": "",
|
||
"topP": "",
|
||
"frequencyPenalty": "",
|
||
"presencePenalty": "",
|
||
"timeout": "",
|
||
"strictToolCalling": "",
|
||
"stopSequence": "",
|
||
"basepath": "",
|
||
"proxyUrl": "",
|
||
"baseOptions": "",
|
||
"allowImageUploads": "",
|
||
"imageResolution": "low",
|
||
"reasoningEffort": "medium",
|
||
"agentModel": "chatOpenAI"
|
||
}
|
||
},
|
||
"outputAnchors": [
|
||
{
|
||
"id": "agentAgentflow_0-output-agentAgentflow",
|
||
"label": "Agent",
|
||
"name": "agentAgentflow"
|
||
}
|
||
],
|
||
"outputs": {},
|
||
"selected": false
|
||
},
|
||
"type": "agentFlow",
|
||
"parentNode": "iterationAgentflow_0",
|
||
"extent": "parent",
|
||
"width": 168,
|
||
"height": 103,
|
||
"selected": false,
|
||
"positionAbsolute": {
|
||
"x": 190.20987564816664,
|
||
"y": 73.16220840535185
|
||
},
|
||
"dragging": false
|
||
},
|
||
{
|
||
"id": "agentAgentflow_1",
|
||
"position": {
|
||
"x": 461.76351005035474,
|
||
"y": 81.71183989476083
|
||
},
|
||
"data": {
|
||
"id": "agentAgentflow_1",
|
||
"label": "Writer Agent",
|
||
"version": 1,
|
||
"name": "agentAgentflow",
|
||
"type": "Agent",
|
||
"color": "#4DD0E1",
|
||
"baseClasses": ["Agent"],
|
||
"category": "Agent Flows",
|
||
"description": "Dynamically choose and utilize tools during runtime, enabling multi-step reasoning",
|
||
"inputParams": [
|
||
{
|
||
"label": "Model",
|
||
"name": "agentModel",
|
||
"type": "asyncOptions",
|
||
"loadMethod": "listModels",
|
||
"loadConfig": true,
|
||
"id": "agentAgentflow_1-input-agentModel-asyncOptions",
|
||
"display": true
|
||
},
|
||
{
|
||
"label": "Messages",
|
||
"name": "agentMessages",
|
||
"type": "array",
|
||
"optional": true,
|
||
"acceptVariable": true,
|
||
"array": [
|
||
{
|
||
"label": "Role",
|
||
"name": "role",
|
||
"type": "options",
|
||
"options": [
|
||
{
|
||
"label": "System",
|
||
"name": "system"
|
||
},
|
||
{
|
||
"label": "Assistant",
|
||
"name": "assistant"
|
||
},
|
||
{
|
||
"label": "Developer",
|
||
"name": "developer"
|
||
},
|
||
{
|
||
"label": "User",
|
||
"name": "user"
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"label": "Content",
|
||
"name": "content",
|
||
"type": "string",
|
||
"acceptVariable": true,
|
||
"generateInstruction": true,
|
||
"rows": 4
|
||
}
|
||
],
|
||
"id": "agentAgentflow_1-input-agentMessages-array",
|
||
"display": true
|
||
},
|
||
{
|
||
"label": "Tools",
|
||
"name": "agentTools",
|
||
"type": "array",
|
||
"optional": true,
|
||
"array": [
|
||
{
|
||
"label": "Tool",
|
||
"name": "agentSelectedTool",
|
||
"type": "asyncOptions",
|
||
"loadMethod": "listTools",
|
||
"loadConfig": true
|
||
},
|
||
{
|
||
"label": "Require Human Input",
|
||
"name": "agentSelectedToolRequiresHumanInput",
|
||
"type": "boolean",
|
||
"optional": true
|
||
}
|
||
],
|
||
"id": "agentAgentflow_1-input-agentTools-array",
|
||
"display": true
|
||
},
|
||
{
|
||
"label": "Knowledge (Document Stores)",
|
||
"name": "agentKnowledgeDocumentStores",
|
||
"type": "array",
|
||
"description": "Give your agent context about different document sources. Document stores must be upserted in advance.",
|
||
"array": [
|
||
{
|
||
"label": "Document Store",
|
||
"name": "documentStore",
|
||
"type": "asyncOptions",
|
||
"loadMethod": "listStores"
|
||
},
|
||
{
|
||
"label": "Describe Knowledge",
|
||
"name": "docStoreDescription",
|
||
"type": "string",
|
||
"generateDocStoreDescription": true,
|
||
"placeholder": "Describe what the knowledge base is about, this is useful for the AI to know when and how to search for correct information",
|
||
"rows": 4
|
||
},
|
||
{
|
||
"label": "Return Source Documents",
|
||
"name": "returnSourceDocuments",
|
||
"type": "boolean",
|
||
"optional": true
|
||
}
|
||
],
|
||
"optional": true,
|
||
"id": "agentAgentflow_1-input-agentKnowledgeDocumentStores-array",
|
||
"display": true
|
||
},
|
||
{
|
||
"label": "Knowledge (Vector Embeddings)",
|
||
"name": "agentKnowledgeVSEmbeddings",
|
||
"type": "array",
|
||
"description": "Give your agent context about different document sources from existing vector stores and embeddings",
|
||
"array": [
|
||
{
|
||
"label": "Vector Store",
|
||
"name": "vectorStore",
|
||
"type": "asyncOptions",
|
||
"loadMethod": "listVectorStores",
|
||
"loadConfig": true
|
||
},
|
||
{
|
||
"label": "Embedding Model",
|
||
"name": "embeddingModel",
|
||
"type": "asyncOptions",
|
||
"loadMethod": "listEmbeddings",
|
||
"loadConfig": true
|
||
},
|
||
{
|
||
"label": "Knowledge Name",
|
||
"name": "knowledgeName",
|
||
"type": "string",
|
||
"placeholder": "A short name for the knowledge base, this is useful for the AI to know when and how to search for correct information"
|
||
},
|
||
{
|
||
"label": "Describe Knowledge",
|
||
"name": "knowledgeDescription",
|
||
"type": "string",
|
||
"placeholder": "Describe what the knowledge base is about, this is useful for the AI to know when and how to search for correct information",
|
||
"rows": 4
|
||
},
|
||
{
|
||
"label": "Return Source Documents",
|
||
"name": "returnSourceDocuments",
|
||
"type": "boolean",
|
||
"optional": true
|
||
}
|
||
],
|
||
"optional": true,
|
||
"id": "agentAgentflow_1-input-agentKnowledgeVSEmbeddings-array",
|
||
"display": true
|
||
},
|
||
{
|
||
"label": "Enable Memory",
|
||
"name": "agentEnableMemory",
|
||
"type": "boolean",
|
||
"description": "Enable memory for the conversation thread",
|
||
"default": true,
|
||
"optional": true,
|
||
"id": "agentAgentflow_1-input-agentEnableMemory-boolean",
|
||
"display": true
|
||
},
|
||
{
|
||
"label": "Memory Type",
|
||
"name": "agentMemoryType",
|
||
"type": "options",
|
||
"options": [
|
||
{
|
||
"label": "All Messages",
|
||
"name": "allMessages",
|
||
"description": "Retrieve all messages from the conversation"
|
||
},
|
||
{
|
||
"label": "Window Size",
|
||
"name": "windowSize",
|
||
"description": "Uses a fixed window size to surface the last N messages"
|
||
},
|
||
{
|
||
"label": "Conversation Summary",
|
||
"name": "conversationSummary",
|
||
"description": "Summarizes the whole conversation"
|
||
},
|
||
{
|
||
"label": "Conversation Summary Buffer",
|
||
"name": "conversationSummaryBuffer",
|
||
"description": "Summarize conversations once token limit is reached. Default to 2000"
|
||
}
|
||
],
|
||
"optional": true,
|
||
"default": "allMessages",
|
||
"show": {
|
||
"agentEnableMemory": true
|
||
},
|
||
"id": "agentAgentflow_1-input-agentMemoryType-options",
|
||
"display": true
|
||
},
|
||
{
|
||
"label": "Window Size",
|
||
"name": "agentMemoryWindowSize",
|
||
"type": "number",
|
||
"default": "20",
|
||
"description": "Uses a fixed window size to surface the last N messages",
|
||
"show": {
|
||
"agentMemoryType": "windowSize"
|
||
},
|
||
"id": "agentAgentflow_1-input-agentMemoryWindowSize-number",
|
||
"display": false
|
||
},
|
||
{
|
||
"label": "Max Token Limit",
|
||
"name": "agentMemoryMaxTokenLimit",
|
||
"type": "number",
|
||
"default": "2000",
|
||
"description": "Summarize conversations once token limit is reached. Default to 2000",
|
||
"show": {
|
||
"agentMemoryType": "conversationSummaryBuffer"
|
||
},
|
||
"id": "agentAgentflow_1-input-agentMemoryMaxTokenLimit-number",
|
||
"display": false
|
||
},
|
||
{
|
||
"label": "Input Message",
|
||
"name": "agentUserMessage",
|
||
"type": "string",
|
||
"description": "Add an input message as user message at the end of the conversation",
|
||
"rows": 4,
|
||
"optional": true,
|
||
"acceptVariable": true,
|
||
"show": {
|
||
"agentEnableMemory": true
|
||
},
|
||
"id": "agentAgentflow_1-input-agentUserMessage-string",
|
||
"display": true
|
||
},
|
||
{
|
||
"label": "Return Response As",
|
||
"name": "agentReturnResponseAs",
|
||
"type": "options",
|
||
"options": [
|
||
{
|
||
"label": "User Message",
|
||
"name": "userMessage"
|
||
},
|
||
{
|
||
"label": "Assistant Message",
|
||
"name": "assistantMessage"
|
||
}
|
||
],
|
||
"default": "userMessage",
|
||
"id": "agentAgentflow_1-input-agentReturnResponseAs-options",
|
||
"display": true
|
||
},
|
||
{
|
||
"label": "Update Flow State",
|
||
"name": "agentUpdateState",
|
||
"description": "Update runtime state during the execution of the workflow",
|
||
"type": "array",
|
||
"optional": true,
|
||
"acceptVariable": true,
|
||
"array": [
|
||
{
|
||
"label": "Key",
|
||
"name": "key",
|
||
"type": "asyncOptions",
|
||
"loadMethod": "listRuntimeStateKeys",
|
||
"freeSolo": true
|
||
},
|
||
{
|
||
"label": "Value",
|
||
"name": "value",
|
||
"type": "string",
|
||
"acceptVariable": true,
|
||
"acceptNodeOutputAsVariable": true
|
||
}
|
||
],
|
||
"id": "agentAgentflow_1-input-agentUpdateState-array",
|
||
"display": true
|
||
}
|
||
],
|
||
"inputAnchors": [],
|
||
"inputs": {
|
||
"agentModel": "chatOpenAI",
|
||
"agentMessages": [
|
||
{
|
||
"role": "system",
|
||
"content": "<p>You are a senior financial analyst. You will be provided with the original query and a set of raw search summaries. Your task is to synthesize these into a long‑form markdown report (at least several paragraphs) including a short executive summary and follow‑up questions</p>"
|
||
}
|
||
],
|
||
"agentTools": "",
|
||
"agentKnowledgeDocumentStores": "",
|
||
"agentEnableMemory": true,
|
||
"agentMemoryType": "allMessages",
|
||
"agentUserMessage": "<p>Original query: <span class=\"variable\" data-type=\"mention\" data-id=\"$form.query\" data-label=\"$form.query\">{{ $form.query }}</span></p><p>Summarized search results: <span class=\"variable\" data-type=\"mention\" data-id=\"iterationAgentflow_0\" data-label=\"iterationAgentflow_0\">{{ iterationAgentflow_0 }}</span></p>",
|
||
"agentReturnResponseAs": "userMessage",
|
||
"agentUpdateState": "",
|
||
"agentModelConfig": {
|
||
"cache": "",
|
||
"modelName": "gpt-4o-mini",
|
||
"temperature": 0.9,
|
||
"streaming": true,
|
||
"maxTokens": "",
|
||
"topP": "",
|
||
"frequencyPenalty": "",
|
||
"presencePenalty": "",
|
||
"timeout": "",
|
||
"strictToolCalling": "",
|
||
"stopSequence": "",
|
||
"basepath": "",
|
||
"proxyUrl": "",
|
||
"baseOptions": "",
|
||
"allowImageUploads": "",
|
||
"imageResolution": "low",
|
||
"reasoningEffort": "medium",
|
||
"agentModel": "chatOpenAI"
|
||
}
|
||
},
|
||
"outputAnchors": [
|
||
{
|
||
"id": "agentAgentflow_1-output-agentAgentflow",
|
||
"label": "Agent",
|
||
"name": "agentAgentflow"
|
||
}
|
||
],
|
||
"outputs": {},
|
||
"selected": false
|
||
},
|
||
"type": "agentFlow",
|
||
"width": 168,
|
||
"height": 71,
|
||
"selected": false,
|
||
"positionAbsolute": {
|
||
"x": 461.76351005035474,
|
||
"y": 81.71183989476083
|
||
},
|
||
"dragging": false
|
||
},
|
||
{
|
||
"id": "stickyNoteAgentflow_0",
|
||
"position": {
|
||
"x": 214.77714507955716,
|
||
"y": -165.2444952661696
|
||
},
|
||
"data": {
|
||
"id": "stickyNoteAgentflow_0",
|
||
"label": "Sticky Note",
|
||
"version": 1,
|
||
"name": "stickyNoteAgentflow",
|
||
"type": "StickyNote",
|
||
"color": "#fee440",
|
||
"baseClasses": ["StickyNote"],
|
||
"category": "Agent Flows",
|
||
"description": "Add notes to the agent flow",
|
||
"inputParams": [
|
||
{
|
||
"label": "",
|
||
"name": "note",
|
||
"type": "string",
|
||
"rows": 1,
|
||
"placeholder": "Type something here",
|
||
"optional": true,
|
||
"id": "stickyNoteAgentflow_0-input-note-string",
|
||
"display": true
|
||
}
|
||
],
|
||
"inputAnchors": [],
|
||
"inputs": {
|
||
"note": "Search Agent will iterate through the search terms and search the web using tool"
|
||
},
|
||
"outputAnchors": [
|
||
{
|
||
"id": "stickyNoteAgentflow_0-output-stickyNoteAgentflow",
|
||
"label": "Sticky Note",
|
||
"name": "stickyNoteAgentflow"
|
||
}
|
||
],
|
||
"outputs": {},
|
||
"selected": false
|
||
},
|
||
"type": "stickyNote",
|
||
"width": 189,
|
||
"height": 142,
|
||
"selected": false,
|
||
"positionAbsolute": {
|
||
"x": 214.77714507955716,
|
||
"y": -165.2444952661696
|
||
},
|
||
"dragging": false
|
||
},
|
||
{
|
||
"id": "stickyNoteAgentflow_1",
|
||
"position": {
|
||
"x": -100.05436009717414,
|
||
"y": -45.56902388417101
|
||
},
|
||
"data": {
|
||
"id": "stickyNoteAgentflow_1",
|
||
"label": "Sticky Note (1)",
|
||
"version": 1,
|
||
"name": "stickyNoteAgentflow",
|
||
"type": "StickyNote",
|
||
"color": "#fee440",
|
||
"baseClasses": ["StickyNote"],
|
||
"category": "Agent Flows",
|
||
"description": "Add notes to the agent flow",
|
||
"inputParams": [
|
||
{
|
||
"label": "",
|
||
"name": "note",
|
||
"type": "string",
|
||
"rows": 1,
|
||
"placeholder": "Type something here",
|
||
"optional": true,
|
||
"id": "stickyNoteAgentflow_1-input-note-string",
|
||
"display": true
|
||
}
|
||
],
|
||
"inputAnchors": [],
|
||
"inputs": {
|
||
"note": "Planner will generate list of search terms to query for"
|
||
},
|
||
"outputAnchors": [
|
||
{
|
||
"id": "stickyNoteAgentflow_1-output-stickyNoteAgentflow",
|
||
"label": "Sticky Note",
|
||
"name": "stickyNoteAgentflow"
|
||
}
|
||
],
|
||
"outputs": {},
|
||
"selected": false
|
||
},
|
||
"type": "stickyNote",
|
||
"width": 189,
|
||
"height": 101,
|
||
"selected": false,
|
||
"positionAbsolute": {
|
||
"x": -100.05436009717414,
|
||
"y": -45.56902388417101
|
||
},
|
||
"dragging": false
|
||
},
|
||
{
|
||
"id": "stickyNoteAgentflow_2",
|
||
"position": {
|
||
"x": 457.98399139175314,
|
||
"y": -35.19227767879839
|
||
},
|
||
"data": {
|
||
"id": "stickyNoteAgentflow_2",
|
||
"label": "Sticky Note (2)",
|
||
"version": 1,
|
||
"name": "stickyNoteAgentflow",
|
||
"type": "StickyNote",
|
||
"color": "#fee440",
|
||
"baseClasses": ["StickyNote"],
|
||
"category": "Agent Flows",
|
||
"description": "Add notes to the agent flow",
|
||
"inputParams": [
|
||
{
|
||
"label": "",
|
||
"name": "note",
|
||
"type": "string",
|
||
"rows": 1,
|
||
"placeholder": "Type something here",
|
||
"optional": true,
|
||
"id": "stickyNoteAgentflow_2-input-note-string",
|
||
"display": true
|
||
}
|
||
],
|
||
"inputAnchors": [],
|
||
"inputs": {
|
||
"note": "Generate the final report from the search results"
|
||
},
|
||
"outputAnchors": [
|
||
{
|
||
"id": "stickyNoteAgentflow_2-output-stickyNoteAgentflow",
|
||
"label": "Sticky Note",
|
||
"name": "stickyNoteAgentflow"
|
||
}
|
||
],
|
||
"outputs": {},
|
||
"selected": false
|
||
},
|
||
"type": "stickyNote",
|
||
"width": 189,
|
||
"height": 101,
|
||
"selected": false,
|
||
"positionAbsolute": {
|
||
"x": 457.98399139175314,
|
||
"y": -35.19227767879839
|
||
},
|
||
"dragging": false
|
||
}
|
||
],
|
||
"edges": [
|
||
{
|
||
"source": "startAgentflow_0",
|
||
"sourceHandle": "startAgentflow_0-output-startAgentflow",
|
||
"target": "llmAgentflow_0",
|
||
"targetHandle": "llmAgentflow_0",
|
||
"data": {
|
||
"sourceColor": "#7EE787",
|
||
"targetColor": "#64B5F6",
|
||
"isHumanInput": false
|
||
},
|
||
"type": "agentFlow",
|
||
"id": "startAgentflow_0-startAgentflow_0-output-startAgentflow-llmAgentflow_0-llmAgentflow_0"
|
||
},
|
||
{
|
||
"source": "llmAgentflow_0",
|
||
"sourceHandle": "llmAgentflow_0-output-llmAgentflow",
|
||
"target": "iterationAgentflow_0",
|
||
"targetHandle": "iterationAgentflow_0",
|
||
"data": {
|
||
"sourceColor": "#64B5F6",
|
||
"targetColor": "#9C89B8",
|
||
"isHumanInput": false
|
||
},
|
||
"type": "agentFlow",
|
||
"id": "llmAgentflow_0-llmAgentflow_0-output-llmAgentflow-iterationAgentflow_0-iterationAgentflow_0"
|
||
},
|
||
{
|
||
"source": "iterationAgentflow_0",
|
||
"sourceHandle": "iterationAgentflow_0-output-iterationAgentflow",
|
||
"target": "agentAgentflow_1",
|
||
"targetHandle": "agentAgentflow_1",
|
||
"data": {
|
||
"sourceColor": "#9C89B8",
|
||
"targetColor": "#4DD0E1",
|
||
"isHumanInput": false
|
||
},
|
||
"type": "agentFlow",
|
||
"id": "iterationAgentflow_0-iterationAgentflow_0-output-iterationAgentflow-agentAgentflow_1-agentAgentflow_1"
|
||
}
|
||
]
|
||
}
|