"en":"During the Agent’s execution, users can actively intervene and interact with the Agent to adjust or guide its output, ensuring the final result aligns with their intentions.",
"de":"Wahrend der Ausführung des Agenten können Benutzer aktiv eingreifen und mit dem Agenten interagieren, um dessen Ausgabe zu steuern, sodass das Endergebnis ihren Vorstellungen entspricht.",
"sys_prompt":"<role>\nYou are the Planning Agent in a multi-agent RAG workflow.\nYour sole job is to design a crisp, executable Search Plan for the next agent. Do not search or answer the user’s question.\n</role>\n<objectives>\nUnderstand the user’s task and decompose it into evidence-seeking steps.\nProduce high-quality queries and retrieval settings tailored to the task type (fact lookup, multi-hop reasoning, comparison, statistics, how-to, etc.).\nIdentify missing information that would materially change the plan (≤3 concise questions).\nOptimize for source trustworthiness, diversity, and recency; define stopping criteria to avoid over-searching.\nAnswer in 150 words.\n<objectives>",
"sys_prompt":"<role>\nYou are the Search Agent.\nYour job is to execute the approved Search Plan, integrate the Await Response feedback, retrieve evidence, and produce a well-grounded answer.\n</role>\n<objectives>\nTranslate the plan + feedback into concrete searches.\nCollect diverse, trustworthy, and recent evidence meeting the plan’s evidence bar.\nSynthesize a concise answer; include citations next to claims they support.\nIf evidence is insufficient or conflicting, clearly state limitations and propose next steps.\n</objectives>\n <tools>\nRetrieval: You must use Retrieval to do the search.\n </tools>\n",
"temperature":0.1,
"temperatureEnabled":false,
"tools":[
{
"component_name":"Retrieval",
"name":"Retrieval",
"params":{
"cross_languages":[],
"description":"",
"empty_response":"",
"kb_ids":[],
"keywords_similarity_weight":0.7,
"outputs":{
"formalized_content":{
"type":"string",
"value":""
},
"json":{
"type":"Array<Object>",
"value":[]
}
},
"rerank_id":"",
"similarity_threshold":0.2,
"toc_enhance":false,
"top_k":1024,
"top_n":8,
"use_kg":false
}
}
],
"topPEnabled":false,
"top_p":0.3,
"user_prompt":"",
"visual_files_var":""
}
},
"upstream":[
"UserFillUp:GoldBroomsRelate"
]
},
"Message:FreshWallsStudy":{
"downstream":[],
"obj":{
"component_name":"Message",
"params":{
"content":[
"{Agent:TangyWordsType@content}"
]
}
},
"upstream":[
"Agent:TangyWordsType"
]
},
"UserFillUp:GoldBroomsRelate":{
"downstream":[
"Agent:TangyWordsType"
],
"obj":{
"component_name":"UserFillUp",
"params":{
"enable_tips":true,
"inputs":{
"instructions":{
"name":"instructions",
"optional":false,
"options":[],
"type":"paragraph"
}
},
"outputs":{
"instructions":{
"name":"instructions",
"optional":false,
"options":[],
"type":"paragraph"
}
},
"tips":"Here is my search plan:\n{Agent:LargeFliesMelt@content}\nAre you okay with it?"
"sys_prompt":"<role>\nYou are the Planning Agent in a multi-agent RAG workflow.\nYour sole job is to design a crisp, executable Search Plan for the next agent. Do not search or answer the user’s question.\n</role>\n<objectives>\nUnderstand the user’s task and decompose it into evidence-seeking steps.\nProduce high-quality queries and retrieval settings tailored to the task type (fact lookup, multi-hop reasoning, comparison, statistics, how-to, etc.).\nIdentify missing information that would materially change the plan (≤3 concise questions).\nOptimize for source trustworthiness, diversity, and recency; define stopping criteria to avoid over-searching.\nAnswer in 150 words.\n<objectives>",
"temperature":0.1,
"temperatureEnabled":false,
"tools":[],
"topPEnabled":false,
"top_p":0.3,
"user_prompt":"",
"visual_files_var":""
},
"label":"Agent",
"name":"Planning Agent"
},
"dragging":false,
"id":"Agent:LargeFliesMelt",
"measured":{
"height":90,
"width":200
},
"position":{
"x":443.96309330796714,
"y":104.61370811205677
},
"selected":false,
"sourcePosition":"right",
"targetPosition":"left",
"type":"agentNode"
},
{
"data":{
"form":{
"enable_tips":true,
"inputs":{
"instructions":{
"name":"instructions",
"optional":false,
"options":[],
"type":"paragraph"
}
},
"outputs":{
"instructions":{
"name":"instructions",
"optional":false,
"options":[],
"type":"paragraph"
}
},
"tips":"Here is my search plan:\n{Agent:LargeFliesMelt@content}\nAre you okay with it?"
"sys_prompt":"<role>\nYou are the Search Agent.\nYour job is to execute the approved Search Plan, integrate the Await Response feedback, retrieve evidence, and produce a well-grounded answer.\n</role>\n<objectives>\nTranslate the plan + feedback into concrete searches.\nCollect diverse, trustworthy, and recent evidence meeting the plan’s evidence bar.\nSynthesize a concise answer; include citations next to claims they support.\nIf evidence is insufficient or conflicting, clearly state limitations and propose next steps.\n</objectives>\n <tools>\nRetrieval: You must use Retrieval to do the search.\n </tools>\n",
"temperature":0.1,
"temperatureEnabled":false,
"tools":[
{
"component_name":"Retrieval",
"name":"Retrieval",
"params":{
"cross_languages":[],
"description":"",
"empty_response":"",
"kb_ids":[],
"keywords_similarity_weight":0.7,
"outputs":{
"formalized_content":{
"type":"string",
"value":""
},
"json":{
"type":"Array<Object>",
"value":[]
}
},
"rerank_id":"",
"similarity_threshold":0.2,
"toc_enhance":false,
"top_k":1024,
"top_n":8,
"use_kg":false
}
}
],
"topPEnabled":false,
"top_p":0.3,
"user_prompt":"",
"visual_files_var":""
},
"label":"Agent",
"name":"Search Agent"
},
"dragging":false,
"id":"Agent:TangyWordsType",
"measured":{
"height":90,
"width":200
},
"position":{
"x":944.6411255659472,
"y":99.84499066368488
},
"selected":true,
"sourcePosition":"right",
"targetPosition":"left",
"type":"agentNode"
},
{
"data":{
"form":{
"description":"This is an agent for a specific task.",
"user_prompt":"This is the order you need to send to the agent."
