{
    "id": 26,
    "title": {
        "en": "Stock Research Report Agent",
        "de": "Aktienanalyse Agent",
        "zh": "股票研究报告智能体"
    },
    "description": {
        "en": "This template helps financial analysts quickly organize information — it can automatically retrieve company data, consolidate financial metrics, and integrate research report insights.",
        "de": "Diese Vorlage hilft Finanzanalysten, Informationen schnell zu organisieren – der Agent kann automatisch Unternehmensdaten abrufen, Finanzkennzahlen konsolidieren und Forschungsberichte integrieren.",
        "zh": "这个模板可以帮助金融分析师快速整理信息——它能够自动获取公司数据、整合财务指标，并汇总研报观点。"
    },
    "canvas_type": "Recommended",
    "dsl": {
            "components": {
                "Agent:ManyToesBrush": {
                    "downstream": [
                        "Switch:FluffyCoinsSell"
                    ],
                    "obj": {
                        "component_name": "Agent",
                        "params": {
                            "cite": true,
                            "delay_after_error": 1,
                            "description": "",
                            "exception_default_value": "",
                            "exception_goto": [],
                            "exception_method": "",
                            "frequencyPenaltyEnabled": false,
                            "frequency_penalty": 0.7,
                            "llm_id": "qwen-turbo-latest@Tongyi-Qianwen",
                            "maxTokensEnabled": false,
                            "max_retries": 3,
                            "max_rounds": 1,
                            "max_tokens": 256,
                            "mcp": [],
                            "message_history_window_size": 12,
                            "outputs": {
                                "content": {
                                    "type": "string",
                                    "value": ""
                                }
                            },
                            "presencePenaltyEnabled": false,
                            "presence_penalty": 0.4,
                            "prompts": [
                                {
                                    "content": "{sys.query}",
                                    "role": "user"
                                }
                            ],
                            "sys_prompt": "<role> \n\nYour responsibility is to identify and extract the stock name or abbreviation from the user's natural language query and return the corresponding unique stock code.\n\n </role> \n\n\n\n<rules> \n\n1. Only one result is allowed: - If a stock is identified \u2192 only return the corresponding stock code; - If no stock is identified \u2192 only return \u201cNot Found\u201d. 2. **Do not** output any additional text, punctuation, explanation, prefixes, or line breaks. 3. The output must strictly adhere to the <response_format>. \n\n</rules>\n\n\n<response_format>\nOnly output the stock code (e.g., AAPL or 600519)\nOr only output \u201cNot Found\u201d\n</response_format>\n\n\n<response_examples>\nUser input: \u201cHelp me check the research report of Apple\u201d \u2192 Output: AAPL\nUser input: \u201cHow is Maotai\u2019s financial performance\u201d \u2192 Output: 600519\nUser input: \u201cHow is the Shanghai Composite Index doing today\u201d \u2192 Output: Not Found\n</response_examples>\n\n\n<tools> - Tavily Search: Use this tool when you are unsure of the stock code. - If you are confident, you do not need to use the tool. \n\n</tools>\n\n\n<strict_output_requirements>\nOnly output the result, no explanations, hints, or notes allowed.\nThe output can only be the stock code or \u201cNot Found\u201d, otherwise, it is considered an incorrect answer.\n</strict_output_requirements>",
                            "temperature": 0.1,
                            "temperatureEnabled": false,
                            "tools": [
                                {
                                    "component_name": "TavilySearch",
                                    "name": "TavilySearch",
                                    "params": {
                                        "api_key": "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",
                                        "days": 7,
                                        "exclude_domains": [],
                                        "include_answer": false,
                                        "include_domains": [],
                                        "include_image_descriptions": false,
                                        "include_images": false,
                                        "include_raw_content": true,
                                        "max_results": 5,
                                        "outputs": {
                                            "formalized_content": {
                                                "type": "string",
                                                "value": ""
                                            },
                                            "json": {
                                                "type": "Array<Object>",
                                                "value": []
                                            }
                                        },
                                        "query": "sys.query",
                                        "search_depth": "basic",
                                        "topic": "general"
                                    }
                                }
                            ],
                            "topPEnabled": false,
                            "top_p": 0.3,
                            "user_prompt": "",
                            "visual_files_var": ""
                        }
                    },
                    "upstream": [
                        "begin"
                    ]
                },
                "Agent:SadDodosRescue": {
                    "downstream": [
                        "Agent:SharpSlothsSlide"
                    ],
                    "obj": {
                        "component_name": "Agent",
                        "params": {
                            "cite": true,
                            "delay_after_error": 1,
                            "description": "",
                            "exception_default_value": "",
                            "exception_goto": [],
                            "exception_method": "",
                            "frequencyPenaltyEnabled": false,
                            "frequency_penalty": 0.7,
                            "llm_id": "kimi-k2-turbo-preview@Moonshot",
                            "maxTokensEnabled": false,
                            "max_retries": 3,
                            "max_rounds": 1,
                            "max_tokens": 256,
                            "mcp": [
                                {
                                    "mcp_id": "30d6ef8ea8d511f0828382e3548809fa",
                                    "tools": {}
                                }
                            ],
                            "message_history_window_size": 12,
                            "outputs": {
                                "content": {
                                    "type": "string",
                                    "value": ""
                                }
                            },
                            "presencePenaltyEnabled": false,
                            "presence_penalty": 0.4,
                            "prompts": [
                                {
                                    "content": "user's query is {sys.query}\n\n\n{Agent:ManyToesBrush@content}\n",
                                    "role": "user"
                                }
                            ],
                            "sys_prompt": "<role> \n\nYou are the information extraction agent. You understand the user\u2019s query and delegate tasks to investoday and the internal research report retrieval agent. \n\n</role> \n\n<requirements>\n\n 1. Based on the stock code output by the \"Extract Stock Code\" agent, call investoday's list_news to retrieve the latest authoritative research reports and views, and save all publicly available key information. \n\n2. Call the \"Internal Research Report Retrieval Agent\" and save the full text of the research report output. \n\n3. Output the content retrieved from investoday and the Internal Research Report Retrieval Agent in full. \n\n</requirements>\n\n\n<report_structure_requirements>\nThe output must be divided into two sections:\n#1. Title: \u201cinvestoday\u201d\nDirectly output the content collected from investoday without any additional processing.\n#2. Title: \"Internal Research Report Retrieval Agent\"\nDirectly output the content provided by the Internal Research Report Retrieval Agent.\n</report_structure_requirements>",
                            "temperature": 0.1,
                            "temperatureEnabled": false,
                            "tools": [
                                {
                                    "component_name": "Agent",
                                    "id": "Agent:MightyIdeasGlow",
                                    "name": "Internal Research Report Retrieval Agent",
                                    "params": {
                                        "cite": true,
                                        "delay_after_error": 1,
                                        "description": "You are a senior financial content analyst who can accurately identify the companies, stock codes, industries or topics mentioned in user questions, and completely extract relevant research content from the knowledge base to ensure that data, opinions and conclusions are not lost.",
                                        "exception_default_value": "",
                                        "exception_goto": [],
                                        "exception_method": "",
                                        "frequencyPenaltyEnabled": false,
                                        "frequency_penalty": 0.7,
                                        "llm_id": "kimi-k2-turbo-preview@Moonshot",
                                        "maxTokensEnabled": false,
                                        "max_retries": 3,
                                        "max_rounds": 1,
                                        "max_tokens": 256,
                                        "mcp": [],
                                        "message_history_window_size": 12,
                                        "outputs": {
                                            "content": {
                                                "type": "string",
                                                "value": ""
                                            }
                                        },
                                        "presencePenaltyEnabled": false,
                                        "presence_penalty": 0.4,
                                        "prompts": [
                                            {
                                                "content": "{sys.query}",
                                                "role": "user"
                                            }
                                        ],
                                        "sys_prompt": "<Task Objective> \n\nRead user input \u2192 Identify the involved company/stock (supports abbreviations, full names, codes, and aliases) \u2192 Retrieve the most relevant research reports from the knowledge base \u2192 Output the full text of the research report, retaining the original format, data, chart descriptions, and risk warnings. \n\n</Task Objective>\n\n\n\n<Execution Rules> \n\n1. Exact Match: Prioritize exact matches of company full names and stock codes. \n\n2. Content Fidelity: Fully retain the research report text stored in the knowledge base without deletion, modification, or omission of paragraphs. \n\n3. Original Data: Retain table data, dates, units, etc., in their original form. \n\n4. Complete Viewpoints: Include investment logic, financial analysis, industry comparisons, earnings forecasts, valuation methods, risk warnings, etc. \n\n5. Merging Multiple Reports: If there are multiple relevant research reports, output them in reverse chronological order. \n\n\n\n6. No Results Feedback: If no matching reports are found, output \u201cNo related research reports available in the knowledge base.\u201d\n\n\n\n </Execution Rules>",
                                        "temperature": 0.1,
                                        "temperatureEnabled": false,
                                        "tools": [
                                            {
                                                "component_name": "Retrieval",
                                                "name": "Retrieval",
                                                "params": {
                                                    "cross_languages": [],
                                                    "description": "A knowledge base of research reports on stock analysis by senior experts",
                                                    "empty_response": "",
                                                    "kb_ids": [
                                                        "60c53ed89acc11f0bc1e7a2a6d0b2755"
                                                    ],
                                                    "keywords_similarity_weight": 0.7,
                                                    "outputs": {
                                                        "formalized_content": {
                                                            "type": "string",
                                                            "value": ""
                                                        }
                                                    },
                                                    "rerank_id": "",
                                                    "similarity_threshold": 0.2,
                                                    "top_k": 1024,
                                                    "top_n": 8,
                                                    "use_kg": false
                                                }
                                            }
                                        ],
                                        "topPEnabled": false,
                                        "top_p": 0.3,
                                        "user_prompt": "This is the order you need to send to the agent.",
                                        "visual_files_var": ""
                                    }
                                }
                            ],
                            "topPEnabled": false,
                            "top_p": 0.3,
                            "user_prompt": "",
                            "visual_files_var": ""
                        }
                    },
                    "upstream": [
                        "Switch:FluffyCoinsSell"
                    ]
                },
                "Agent:SharpSlothsSlide": {
                    "downstream": [
                        "Message:OliveLawsArgue"
                    ],
                    "obj": {
                        "component_name": "Agent",
                        "params": {
                            "cite": true,
                            "delay_after_error": 1,
                            "description": "",
                            "exception_default_value": "",
                            "exception_goto": [],
                            "exception_method": "",
                            "frequencyPenaltyEnabled": false,
                            "frequency_penalty": 0.7,
                            "llm_id": "qwen-turbo-latest@Tongyi-Qianwen",
                            "maxTokensEnabled": false,
                            "max_retries": 3,
                            "max_rounds": 1,
                            "max_tokens": 256,
                            "mcp": [],
                            "message_history_window_size": 12,
                            "outputs": {
                                "content": {
                                    "type": "string",
                                    "value": ""
                                }
                            },
                            "presencePenaltyEnabled": false,
                            "presence_penalty": 0.4,
                            "prompts": [
                                {
                                    "content": "User query questions:\n\n\n\n{sys.query}\n\n\n\nInformation Extraction Agent:\n\n{Agent:SadDodosRescue@content}",
                                    "role": "user"
                                }
                            ],
                            "sys_prompt": "<role> \n\nYou are a senior investment banking (IB) analyst with years of experience in capital market research. You excel at writing investment research reports covering publicly listed companies, industries, and macroeconomics. You possess strong financial analysis skills and industry insights, combining quantitative and qualitative analysis to provide high-value references for investment decisions. \n\n**You are able to retain and present differentiated viewpoints from various reports and sources in your research, and when discrepancies arise, you do not merge them into a single conclusion. Instead, you compare and analyze the differences.** \n\n\n</role> \n\n\n\n\n<input> \n\nYou will receive financial information extracted by the information extraction agent.\n\n </input>\n\n\n<core_task>\nBased on the content returned by the information extraction agent (no fabrication of data), write a professional, complete, and structured investment research report. The report must be logically rigorous, clearly organized, and use professional language, suitable for reference by fund managers, institutional investors, and other professional readers.\nWhen there are differences in analysis or forecasts between different reports or institutions, you must list and identify the sources in the report. You should not select only one viewpoint. You need to point out the differences, their possible causes, and their impact on investment judgments.\n</core_task>\n\n\n<report_structure_requirements>\n##1. Summary\nProvide a concise overview of the company\u2019s core business, recent performance, industry positioning, and major investment highlights.\nSummarize key conclusions in 3-5 sentences.\nHighlight any discrepancies in core conclusions and briefly describe the differing viewpoints and areas of disagreement.\n##2. Company Overview\nDescribe the company's main business, core products/services, market share, competitive advantages, and business model.\nHighlight any differences in the description of the company\u2019s market position or competitive advantages from different sources. Present and compare these differences.\n##3. Recent Financial Performance\nSummarize key metrics from the latest financial report (e.g., revenue, net profit, gross margin, EPS).\nHighlight the drivers behind the trends and compare the differential analyses from different reports. Present this comparison in a table.\n##4. Industry Trends & Opportunities\nOverview of industry development trends, market size, and major drivers.\nIf different sources provide differing forecasts for industry growth rates, technological trends, or competitive landscape, list these and provide background information. Present this comparison in a table.\n##5. Investment Recommendation\nProvide a clear investment recommendation based on the analysis above (e.g., \"Buy/Hold/Neutral/Sell\"), presented in a table.\nInclude investment ratings or recommendations from all sources, with the source and date clearly noted.\nIf you provide a combined recommendation based on different viewpoints, clearly explain the reasoning behind this integration.\n##6. Appendix & References\nList the data sources, analysis methods, important formulas, or chart descriptions used.\nAll references must come from the information extraction agent and the company financial data table provided, or publicly noted sources.\nFor differentiated viewpoints, provide full citation information (author, institution, date) and present this in a table.\n</report_structure_requirements>\n\n\n<output_requirements>\nLanguage Style: Financial, professional, precise, and analytical.\nViewpoint Retention: When there are multiple viewpoints and conclusions, all must be retained and compared. You cannot choose only one.\nCitations: When specific data or viewpoints are referenced, include the source in parentheses (e.g., Source: Morgan Stanley Research, 2024-05-07).\nFacts: All data and conclusions must come from the information extraction agent or their noted legitimate sources. No fabrication is allowed.\nReadability: Use short paragraphs and bullet points to make it easy for professional readers to grasp key information and see the differences in viewpoints.\n</output_requirements>\n\n\n<output_goal>\nGenerate a complete investment research report that meets investment banking industry standards, which can be directly used for institutional investment internal reference, while faithfully retaining differentiated viewpoints from various reports and providing the corresponding analysis.\n</output_goal>",
                            "temperature": 0.1,
                            "temperatureEnabled": false,
                            "tools": [],
                            "topPEnabled": false,
                            "top_p": 0.3,
                            "user_prompt": "",
                            "visual_files_var": ""
                        }
                    },
                    "upstream": [
                        "Agent:SadDodosRescue"
                    ]
                },
                "CodeExec:LightSheepTrade": {
                    "downstream": [
                        "Message:OliveLawsArgue"
                    ],
                    "obj": {
                        "component_name": "CodeExec",
                        "params": {
                            "arguments": {
                                "input_text": "YahooFinance:QuickAdsDig@report"
                            },
                            "lang": "python",
                            "outputs": {
                                "md_table": {
                                    "type": "String",
                                    "value": ""
                                }
                            },
                            "script": "import re\n\ndef format_number(value: str) -> str:\n    \"\"\"Convert scientific notation or floating-point numbers to comma-separated numbers\"\"\"\n    try:\n        num = float(value)\n        if num.is_integer():\n            return f\"{int(num):,}\"  # If it's an integer, format without decimal places\n        else:\n            return f\"{num:,.2f}\"  # Otherwise, keep two decimal places and add commas\n    except:\n        return value  # Return the original value if it's not a number (e.g., \u2014 or empty)\n\ndef extract_md_table_single_column(input_text: str) -> str:\n    # Use English indicators directly\n    indicators = [\n        \"Total Assets\", \"Total Equity\", \"Tangible Book Value\", \"Total Debt\", \n        \"Net Debt\", \"Cash And Cash Equivalents\", \"Working Capital\", \n        \"Long Term Debt\", \"Common Stock Equity\", \"Ordinary Shares Number\"\n    ]\n    \n    # Core indicators and their corresponding units\n    unit_map = {\n        \"Total Assets\": \"USD\",\n        \"Total Equity\": \"USD\",\n        \"Tangible Book Value\": \"USD\",\n        \"Total Debt\": \"USD\",\n        \"Net Debt\": \"USD\",\n        \"Cash And Cash Equivalents\": \"USD\",\n        \"Working Capital\": \"USD\",\n        \"Long Term Debt\": \"USD\",\n        \"Common Stock Equity\": \"USD\",\n        \"Ordinary Shares Number\": \"Shares\"\n    }\n\n    lines = input_text.splitlines()\n\n    # Automatically detect the date column, keeping only the first one\n    date_pattern = r\"\\d{4}-\\d{2}-\\d{2}\"\n    header_line = \"\"\n    for line in lines:\n        if re.search(date_pattern, line):\n            header_line = line\n            break\n\n    if not header_line:\n        raise ValueError(\"Date column header row not found\")\n\n    dates = re.findall(date_pattern, header_line)\n    first_date = dates[0]  # Keep only the first date\n    header = f\"| Indicator | {first_date} |\"\n    divider = \"|------------------------|------------|\"\n\n    rows = []\n    for ind in indicators:\n        unit = unit_map.get(ind, \"\")\n        display_ind = f\"{ind} ({unit})\" if unit else ind\n\n        found = False\n        for line in lines:\n            if ind in line:\n                # Match numbers and possible units\n                pattern = r\"(nan|[0-9\\.]+(?:[eE][+-]?\\d+)?)\"\n                values = re.findall(pattern, line)\n                # Replace 'nan' with '\u2014' and format the number\n                first_value = values[0].strip() if values and values[0].strip().lower() != \"nan\" else \"\u2014\"\n                first_value = format_number(first_value) if first_value != \"\u2014\" else \"\u2014\"\n                rows.append(f\"| {display_ind} | {first_value} |\")\n                found = True\n                break\n        if not found:\n            rows.append(f\"| {display_ind} | \u2014 |\")\n\n    md_table = \"\\n\".join([header, divider] + rows)\n    return md_table\n\ndef main(input_text: str):\n    return extract_md_table_single_column(input_text)\n"
                        }
                    },
                    "upstream": [
                        "YahooFinance:QuickAdsDig"
                    ]
                },
                "Message:OliveLawsArgue": {
                    "downstream": [],
                    "obj": {
                        "component_name": "Message",
                        "params": {
                            "content": [
                                "Company financial statements:\n\n{CodeExec:LightSheepTrade@md_table}\n\n\n{Agent:SharpSlothsSlide@content}"
                            ]
                        }
                    },
                    "upstream": [
                        "Agent:SharpSlothsSlide",
                        "CodeExec:LightSheepTrade"
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                },
                "Message:TwentyBanksLeave": {
                    "downstream": [],
                    "obj": {
                        "component_name": "Message",
                        "params": {
                            "content": [
                                "Your query is not supported."
                            ]
                        }
                    },
                    "upstream": [
                        "Switch:FluffyCoinsSell"
                    ]
                },
                "Switch:FluffyCoinsSell": {
                    "downstream": [
                        "YahooFinance:QuickAdsDig",
                        "Agent:SadDodosRescue",
                        "Message:TwentyBanksLeave"
                    ],
                    "obj": {
                        "component_name": "Switch",
                        "params": {
                            "conditions": [
                                {
                                    "items": [
                                        {
                                            "cpn_id": "Agent:ManyToesBrush@content",
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                                    "logical_operator": "and",
                                    "to": [
                                        "YahooFinance:QuickAdsDig",
                                        "Agent:SadDodosRescue"
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                                "Message:TwentyBanksLeave"
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                    "upstream": [
                        "Agent:ManyToesBrush"
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                },
                "YahooFinance:QuickAdsDig": {
                    "downstream": [
                        "CodeExec:LightSheepTrade"
                    ],
                    "obj": {
                        "component_name": "YahooFinance",
                        "params": {
                            "balance_sheet": true,
                            "cash_flow_statement": false,
                            "financials": false,
                            "history": false,
                            "info": false,
                            "news": false,
                            "outputs": {
                                "report": {
                                    "type": "string",
                                    "value": ""
                                }
                            },
                            "stock_code": "sys.query"
                        }
                    },
                    "upstream": [
                        "Switch:FluffyCoinsSell"
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                },
                "begin": {
                    "downstream": [
                        "Agent:ManyToesBrush"
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                    "obj": {
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                        "params": {
                            "enablePrologue": true,
                            "inputs": {},
                            "mode": "conversational",
                            "prologue": "Hi! I'm your assistant. What can I do for you?"
                        }
                    },
                    "upstream": []
                }
            },
            "globals": {
                "sys.conversation_turns": 0,
                "sys.files": [],
                "sys.query": "",
                "sys.user_id": ""
            },
            "graph": {
                "edges": [
                    {
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                            "isHovered": false
                        },
                        "id": "xy-edge__Agent:ManyToesBrushtool-Tool:AngryRabbitsPlayend",
                        "source": "Agent:ManyToesBrush",
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                        "targetHandle": "end"
                    },
                    {
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                        "data": {
                            "isHovered": false
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                        "id": "xy-edge__Agent:MightyIdeasGlowtool-Tool:FullIconsStopend",
                        "source": "Agent:MightyIdeasGlow",
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                        "target": "Tool:FullIconsStop",
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                        "source": "begin",
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                        "sourceHandle": "start",
                        "target": "Switch:FluffyCoinsSell",
                        "targetHandle": "end"
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                    {
                        "data": {
                            "isHovered": false
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                        "id": "xy-edge__Switch:FluffyCoinsSellCase 1-YahooFinance:QuickAdsDigend",
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                    {
                        "data": {
                            "isHovered": false
                        },
                        "id": "xy-edge__Switch:FluffyCoinsSellCase 1-Agent:SadDodosRescueend",
                        "markerEnd": "logo",
                        "source": "Switch:FluffyCoinsSell",
                        "sourceHandle": "Case 1",
                        "style": {
                            "stroke": "rgba(151, 154, 171, 1)",
                            "strokeWidth": 1
                        },
                        "target": "Agent:SadDodosRescue",
                        "targetHandle": "end",
                        "type": "buttonEdge",
                        "zIndex": 1001
                    },
                    {
                        "data": {
                            "isHovered": false
                        },
                        "id": "xy-edge__Switch:FluffyCoinsSellend_cpn_ids-Message:TwentyBanksLeaveend",
                        "markerEnd": "logo",
                        "source": "Switch:FluffyCoinsSell",
                        "sourceHandle": "end_cpn_ids",
                        "style": {
                            "stroke": "rgba(151, 154, 171, 1)",
                            "strokeWidth": 1
                        },
                        "target": "Message:TwentyBanksLeave",
                        "targetHandle": "end",
                        "type": "buttonEdge",
                        "zIndex": 1001
                    },
                    {
                        "data": {
                            "isHovered": false
                        },
                        "id": "xy-edge__CodeExec:LightSheepTradestart-Message:OliveLawsArgueend",
                        "markerEnd": "logo",
                        "source": "CodeExec:LightSheepTrade",
                        "sourceHandle": "start",
                        "style": {
                            "stroke": "rgba(91, 93, 106, 1)",
                            "strokeWidth": 1
                        },
                        "target": "Message:OliveLawsArgue",
                        "targetHandle": "end",
                        "type": "buttonEdge",
                        "zIndex": 1001
                    },
                    {
                        "data": {
                            "isHovered": false
                        },
                        "id": "xy-edge__Agent:SadDodosRescuetool-Tool:ClearKiwisRollend",
                        "source": "Agent:SadDodosRescue",
                        "sourceHandle": "tool",
                        "target": "Tool:ClearKiwisRoll",
                        "targetHandle": "end"
                    }
                ],
                "nodes": [
                    {
                        "data": {
                            "form": {
                                "enablePrologue": true,
                                "inputs": {},
                                "mode": "conversational",
                                "prologue": "Hi! I'm your assistant. What can I do for you?"
                            },
                            "label": "Begin",
                            "name": "begin"
                        },
                        "dragging": false,
                        "id": "begin",
                        "measured": {
                            "height": 48,
                            "width": 200
                        },
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                            "x": -250.58492312820874,
                            "y": 304.13718826989873
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                                "cite": true,
                                "delay_after_error": 1,
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                                "exception_default_value": "",
                                "exception_goto": [],
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                                "llm_id": "qwen-turbo-latest@Tongyi-Qianwen",
                                "maxTokensEnabled": false,
                                "max_retries": 3,
                                "max_rounds": 1,
                                "max_tokens": 256,
                                "mcp": [],
                                "message_history_window_size": 12,
                                "outputs": {
                                    "content": {
                                        "type": "string",
                                        "value": ""
                                    }
                                },
                                "presencePenaltyEnabled": false,
                                "presence_penalty": 0.4,
                                "prompts": [
                                    {
                                        "content": "{sys.query}",
                                        "role": "user"
                                    }
                                ],
                                "sys_prompt": "<role> \n\nYour responsibility is to identify and extract the stock name or abbreviation from the user's natural language query and return the corresponding unique stock code.\n\n </role> \n\n\n\n<rules> \n\n1. Only one result is allowed: - If a stock is identified \u2192 only return the corresponding stock code; - If no stock is identified \u2192 only return \u201cNot Found\u201d. 2. **Do not** output any additional text, punctuation, explanation, prefixes, or line breaks. 3. The output must strictly adhere to the <response_format>. \n\n</rules>\n\n\n<response_format>\nOnly output the stock code (e.g., AAPL or 600519)\nOr only output \u201cNot Found\u201d\n</response_format>\n\n\n<response_examples>\nUser input: \u201cHelp me check the research report of Apple\u201d \u2192 Output: AAPL\nUser input: \u201cHow is Maotai\u2019s financial performance\u201d \u2192 Output: 600519\nUser input: \u201cHow is the Shanghai Composite Index doing today\u201d \u2192 Output: Not Found\n</response_examples>\n\n\n<tools> - Tavily Search: Use this tool when you are unsure of the stock code. - If you are confident, you do not need to use the tool. \n\n</tools>\n\n\n<strict_output_requirements>\nOnly output the result, no explanations, hints, or notes allowed.\nThe output can only be the stock code or \u201cNot Found\u201d, otherwise, it is considered an incorrect answer.\n</strict_output_requirements>",
                                "temperature": 0.1,
                                "temperatureEnabled": false,
                                "tools": [
                                    {
                                        "component_name": "TavilySearch",
                                        "name": "TavilySearch",
                                        "params": {
                                            "api_key": "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",
                                            "days": 7,
                                            "exclude_domains": [],
                                            "include_answer": false,
                                            "include_domains": [],
                                            "include_image_descriptions": false,
                                            "include_images": false,
                                            "include_raw_content": true,
                                            "max_results": 5,
                                            "outputs": {
                                                "formalized_content": {
                                                    "type": "string",
                                                    "value": ""
                                                },
                                                "json": {
                                                    "type": "Array<Object>",
                                                    "value": []
                                                }
                                            },
                                            "query": "sys.query",
                                            "search_depth": "basic",
                                            "topic": "general"
                                        }
                                    }
                                ],
                                "topPEnabled": false,
                                "top_p": 0.3,
                                "user_prompt": "",
                                "visual_files_var": ""
                            },
                            "label": "Agent",
                            "name": "Extract Stock Code Agent"
                        },
                        "dragging": false,
                        "id": "Agent:ManyToesBrush",
                        "measured": {
                            "height": 76,
                            "width": 200
                        },
                        "position": {
                            "x": 1.784314979916303,
                            "y": 285.7261182739586
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                        "selected": false,
                        "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."
                            },
                            "label": "Tool",
                            "name": "flow.tool_0"
                        },
                        "dragging": false,
                        "id": "Tool:AngryRabbitsPlay",
                        "measured": {
                            "height": 48,
                            "width": 200
                        },
                        "position": {
                            "x": -1.1174997064789522,
                            "y": 392.2709327777357
                        },
                        "selected": false,
                        "sourcePosition": "right",
                        "targetPosition": "left",
                        "type": "toolNode"
                    },
                    {
                        "data": {
                            "form": {
                                "content": [
                                    "Your query is not supported."
                                ]
                            },
                            "label": "Message",
                            "name": "Reply to irrelevant message node"
                        },
                        "dragging": false,
                        "id": "Message:TwentyBanksLeave",
                        "measured": {
                            "height": 48,
                            "width": 200
                        },
                        "position": {
                            "x": 1274.991898394738,
                            "y": 540.2215056031129
                        },
                        "selected": false,
                        "sourcePosition": "right",
                        "targetPosition": "left",
                        "type": "messageNode"
                    },
                    {
                        "data": {
                            "form": {
                                "balance_sheet": true,
                                "cash_flow_statement": false,
                                "financials": false,
                                "history": false,
                                "info": false,
                                "news": false,
                                "outputs": {
                                    "report": {
                                        "type": "string",
                                        "value": ""
                                    }
                                },
                                "stock_code": "sys.query"
                            },
                            "label": "YahooFinance",
                            "name": "YahooFinance"
                        },
                        "dragging": false,
                        "id": "YahooFinance:QuickAdsDig",
                        "measured": {
                            "height": 48,
                            "width": 200
                        },
                        "position": {
                            "x": 676.5378050046916,
                            "y": 74.09222900489664
                        },
                        "selected": false,
                        "sourcePosition": "right",
                        "targetPosition": "left",
                        "type": "ragNode"
                    },
                    {
                        "data": {
                            "form": {
                                "cite": true,
                                "delay_after_error": 1,
                                "description": "",
                                "exception_default_value": "",
                                "exception_goto": [],
                                "exception_method": "",
                                "frequencyPenaltyEnabled": false,
                                "frequency_penalty": 0.7,
                                "llm_id": "kimi-k2-turbo-preview@Moonshot",
                                "maxTokensEnabled": false,
                                "max_retries": 3,
                                "max_rounds": 1,
                                "max_tokens": 256,
                                "mcp": [
                                    {
                                        "mcp_id": "30d6ef8ea8d511f0828382e3548809fa",
                                        "tools": {}
                                    }
                                ],
                                "message_history_window_size": 12,
                                "outputs": {
                                    "content": {
                                        "type": "string",
                                        "value": ""
                                    }
                                },
                                "presencePenaltyEnabled": false,
                                "presence_penalty": 0.4,
                                "prompts": [
                                    {
                                        "content": "user's query is {sys.query}\n\n\n{Agent:ManyToesBrush@content}\n",
                                        "role": "user"
                                    }
                                ],
                                "sys_prompt": "<role> \n\nYou are the information extraction agent. You understand the user\u2019s query and delegate tasks to investoday and the internal research report retrieval agent. \n\n</role> \n\n<requirements>\n\n 1. Based on the stock code output by the \"Extract Stock Code\" agent, call investoday's list_news to retrieve the latest authoritative research reports and views, and save all publicly available key information. \n\n2. Call the \"Internal Research Report Retrieval Agent\" and save the full text of the research report output. \n\n3. Output the content retrieved from investoday and the Internal Research Report Retrieval Agent in full. \n\n</requirements>\n\n\n<report_structure_requirements>\nThe output must be divided into two sections:\n#1. Title: \u201cinvestoday\u201d\nDirectly output the content collected from investoday without any additional processing.\n#2. Title: \"Internal Research Report Retrieval Agent\"\nDirectly output the content provided by the Internal Research Report Retrieval Agent.\n</report_structure_requirements>",
                                "temperature": 0.1,
                                "temperatureEnabled": false,
                                "tools": [],
                                "topPEnabled": false,
                                "top_p": 0.3,
                                "user_prompt": "",
                                "visual_files_var": ""
                            },
                            "label": "Agent",
                            "name": "Information Extraction Agent"
                        },
                        "dragging": false,
                        "id": "Agent:SadDodosRescue",
                        "measured": {
                            "height": 76,
                            "width": 200
                        },
                        "position": {
                            "x": 674.0210917308762,
                            "y": 154.63747017677127
                        },
                        "selected": false,
                        "sourcePosition": "right",
                        "targetPosition": "left",
                        "type": "agentNode"
                    },
                    {
                        "data": {
                            "form": {
                                "arguments": {
                                    "input_text": "YahooFinance:QuickAdsDig@report"
                                },
                                "lang": "python",
                                "outputs": {
                                    "md_table": {
                                        "type": "String",
                                        "value": ""
                                    }
                                },
                                "script": "import re\n\ndef format_number(value: str) -> str:\n    \"\"\"Convert scientific notation or floating-point numbers to comma-separated numbers\"\"\"\n    try:\n        num = float(value)\n        if num.is_integer():\n            return f\"{int(num):,}\"  # If it's an integer, format without decimal places\n        else:\n            return f\"{num:,.2f}\"  # Otherwise, keep two decimal places and add commas\n    except:\n        return value  # Return the original value if it's not a number (e.g., \u2014 or empty)\n\ndef extract_md_table_single_column(input_text: str) -> str:\n    # Use English indicators directly\n    indicators = [\n        \"Total Assets\", \"Total Equity\", \"Tangible Book Value\", \"Total Debt\", \n        \"Net Debt\", \"Cash And Cash Equivalents\", \"Working Capital\", \n        \"Long Term Debt\", \"Common Stock Equity\", \"Ordinary Shares Number\"\n    ]\n    \n    # Core indicators and their corresponding units\n    unit_map = {\n        \"Total Assets\": \"USD\",\n        \"Total Equity\": \"USD\",\n        \"Tangible Book Value\": \"USD\",\n        \"Total Debt\": \"USD\",\n        \"Net Debt\": \"USD\",\n        \"Cash And Cash Equivalents\": \"USD\",\n        \"Working Capital\": \"USD\",\n        \"Long Term Debt\": \"USD\",\n        \"Common Stock Equity\": \"USD\",\n        \"Ordinary Shares Number\": \"Shares\"\n    }\n\n    lines = input_text.splitlines()\n\n    # Automatically detect the date column, keeping only the first one\n    date_pattern = r\"\\d{4}-\\d{2}-\\d{2}\"\n    header_line = \"\"\n    for line in lines:\n        if re.search(date_pattern, line):\n            header_line = line\n            break\n\n    if not header_line:\n        raise ValueError(\"Date column header row not found\")\n\n    dates = re.findall(date_pattern, header_line)\n    first_date = dates[0]  # Keep only the first date\n    header = f\"| Indicator | {first_date} |\"\n    divider = \"|------------------------|------------|\"\n\n    rows = []\n    for ind in indicators:\n        unit = unit_map.get(ind, \"\")\n        display_ind = f\"{ind} ({unit})\" if unit else ind\n\n        found = False\n        for line in lines:\n            if ind in line:\n                # Match numbers and possible units\n                pattern = r\"(nan|[0-9\\.]+(?:[eE][+-]?\\d+)?)\"\n                values = re.findall(pattern, line)\n                # Replace 'nan' with '\u2014' and format the number\n                first_value = values[0].strip() if values and values[0].strip().lower() != \"nan\" else \"\u2014\"\n                first_value = format_number(first_value) if first_value != \"\u2014\" else \"\u2014\"\n                rows.append(f\"| {display_ind} | {first_value} |\")\n                found = True\n                break\n        if not found:\n            rows.append(f\"| {display_ind} | \u2014 |\")\n\n    md_table = \"\\n\".join([header, divider] + rows)\n    return md_table\n\ndef main(input_text: str):\n    return extract_md_table_single_column(input_text)\n"
                            },
                            "label": "CodeExec",
                            "name": "Code-generated balance sheet"
                        },
                        "dragging": false,
                        "id": "CodeExec:LightSheepTrade",
                        "measured": {
                            "height": 48,
                            "width": 200
                        },
                        "position": {
                            "x": 970.444642975358,
                            "y": 74.04386270784316
                        },
                        "selected": false,
                        "sourcePosition": "right",
                        "targetPosition": "left",
                        "type": "ragNode"
                    },
                    {
                        "data": {
                            "form": {
                                "cite": true,
                                "delay_after_error": 1,
                                "description": "",
                                "exception_default_value": "",
                                "exception_goto": [],
                                "exception_method": "",
                                "frequencyPenaltyEnabled": false,
                                "frequency_penalty": 0.7,
                                "llm_id": "qwen-turbo-latest@Tongyi-Qianwen",
                                "maxTokensEnabled": false,
                                "max_retries": 3,
                                "max_rounds": 1,
                                "max_tokens": 256,
                                "mcp": [],
                                "message_history_window_size": 12,
                                "outputs": {
                                    "content": {
                                        "type": "string",
                                        "value": ""
                                    }
                                },
                                "presencePenaltyEnabled": false,
                                "presence_penalty": 0.4,
                                "prompts": [
                                    {
                                        "content": "User query questions:\n\n\n\n{sys.query}\n\n\n\nInformation Extraction Agent:\n\n{Agent:SadDodosRescue@content}",
                                        "role": "user"
                                    }
                                ],
                                "sys_prompt": "<role> \n\nYou are a senior investment banking (IB) analyst with years of experience in capital market research. You excel at writing investment research reports covering publicly listed companies, industries, and macroeconomics. You possess strong financial analysis skills and industry insights, combining quantitative and qualitative analysis to provide high-value references for investment decisions. \n\n**You are able to retain and present differentiated viewpoints from various reports and sources in your research, and when discrepancies arise, you do not merge them into a single conclusion. Instead, you compare and analyze the differences.** \n\n\n</role> \n\n\n\n\n<input> \n\nYou will receive financial information extracted by the information extraction agent.\n\n </input>\n\n\n<core_task>\nBased on the content returned by the information extraction agent (no fabrication of data), write a professional, complete, and structured investment research report. The report must be logically rigorous, clearly organized, and use professional language, suitable for reference by fund managers, institutional investors, and other professional readers.\nWhen there are differences in analysis or forecasts between different reports or institutions, you must list and identify the sources in the report. You should not select only one viewpoint. You need to point out the differences, their possible causes, and their impact on investment judgments.\n</core_task>\n\n\n<report_structure_requirements>\n##1. Summary\nProvide a concise overview of the company\u2019s core business, recent performance, industry positioning, and major investment highlights.\nSummarize key conclusions in 3-5 sentences.\nHighlight any discrepancies in core conclusions and briefly describe the differing viewpoints and areas of disagreement.\n##2. Company Overview\nDescribe the company's main business, core products/services, market share, competitive advantages, and business model.\nHighlight any differences in the description of the company\u2019s market position or competitive advantages from different sources. Present and compare these differences.\n##3. Recent Financial Performance\nSummarize key metrics from the latest financial report (e.g., revenue, net profit, gross margin, EPS).\nHighlight the drivers behind the trends and compare the differential analyses from different reports. Present this comparison in a table.\n##4. Industry Trends & Opportunities\nOverview of industry development trends, market size, and major drivers.\nIf different sources provide differing forecasts for industry growth rates, technological trends, or competitive landscape, list these and provide background information. Present this comparison in a table.\n##5. Investment Recommendation\nProvide a clear investment recommendation based on the analysis above (e.g., \"Buy/Hold/Neutral/Sell\"), presented in a table.\nInclude investment ratings or recommendations from all sources, with the source and date clearly noted.\nIf you provide a combined recommendation based on different viewpoints, clearly explain the reasoning behind this integration.\n##6. Appendix & References\nList the data sources, analysis methods, important formulas, or chart descriptions used.\nAll references must come from the information extraction agent and the company financial data table provided, or publicly noted sources.\nFor differentiated viewpoints, provide full citation information (author, institution, date) and present this in a table.\n</report_structure_requirements>\n\n\n<output_requirements>\nLanguage Style: Financial, professional, precise, and analytical.\nViewpoint Retention: When there are multiple viewpoints and conclusions, all must be retained and compared. You cannot choose only one.\nCitations: When specific data or viewpoints are referenced, include the source in parentheses (e.g., Source: Morgan Stanley Research, 2024-05-07).\nFacts: All data and conclusions must come from the information extraction agent or their noted legitimate sources. No fabrication is allowed.\nReadability: Use short paragraphs and bullet points to make it easy for professional readers to grasp key information and see the differences in viewpoints.\n</output_requirements>\n\n\n<output_goal>\nGenerate a complete investment research report that meets investment banking industry standards, which can be directly used for institutional investment internal reference, while faithfully retaining differentiated viewpoints from various reports and providing the corresponding analysis.\n</output_goal>",
                                "temperature": 0.1,
                                "temperatureEnabled": false,
                                "tools": [],
                                "topPEnabled": false,
                                "top_p": 0.3,
                                "user_prompt": "",
                                "visual_files_var": ""
                            },
                            "label": "Agent",
                            "name": "Research report generation agent"
                        },
                        "id": "Agent:SharpSlothsSlide",
                        "measured": {
                            "height": 76,
                            "width": 200
                        },
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                            "x": 974.0210917308762,
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                        "sourcePosition": "right",
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                        "type": "agentNode"
                    },
                    {
                        "data": {
                            "form": {
                                "content": [
                                    "Company financial statements:\n\n{CodeExec:LightSheepTrade@md_table}\n\n\n{Agent:SharpSlothsSlide@content}"
                                ]
                            },
                            "label": "Message",
                            "name": "Reply message node"
                        },
                        "dragging": false,
                        "id": "Message:OliveLawsArgue",
                        "measured": {
                            "height": 48,
                            "width": 200
                        },
                        "position": {
                            "x": 1279.3354680249918,
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                        "sourcePosition": "right",
                        "targetPosition": "left",
                        "type": "messageNode"
                    },
                    {
                        "data": {
                            "form": {
                                "cite": true,
                                "delay_after_error": 1,
                                "description": "You are a senior financial content analyst who can accurately identify the companies, stock codes, industries or topics mentioned in user questions, and completely extract relevant research content from the knowledge base to ensure that data, opinions and conclusions are not lost.",
                                "exception_default_value": "",
                                "exception_goto": [],
                                "exception_method": "",
                                "frequencyPenaltyEnabled": false,
                                "frequency_penalty": 0.7,
                                "llm_id": "kimi-k2-turbo-preview@Moonshot",
                                "maxTokensEnabled": false,
                                "max_retries": 3,
                                "max_rounds": 1,
                                "max_tokens": 256,
                                "mcp": [],
                                "message_history_window_size": 12,
                                "outputs": {
                                    "content": {
                                        "type": "string",
                                        "value": ""
                                    }
                                },
                                "presencePenaltyEnabled": false,
                                "presence_penalty": 0.4,
                                "prompts": [
                                    {
                                        "content": "{sys.query}",
                                        "role": "user"
                                    }
                                ],
                                "sys_prompt": "<Task Objective> \n\nRead user input \u2192 Identify the involved company/stock (supports abbreviations, full names, codes, and aliases) \u2192 Retrieve the most relevant research reports from the knowledge base \u2192 Output the full text of the research report, retaining the original format, data, chart descriptions, and risk warnings. \n\n</Task Objective>\n\n\n\n<Execution Rules> \n\n1. Exact Match: Prioritize exact matches of company full names and stock codes. \n\n2. Content Fidelity: Fully retain the research report text stored in the knowledge base without deletion, modification, or omission of paragraphs. \n\n3. Original Data: Retain table data, dates, units, etc., in their original form. \n\n4. Complete Viewpoints: Include investment logic, financial analysis, industry comparisons, earnings forecasts, valuation methods, risk warnings, etc. \n\n5. Merging Multiple Reports: If there are multiple relevant research reports, output them in reverse chronological order. \n\n\n\n6. No Results Feedback: If no matching reports are found, output \u201cNo related research reports available in the knowledge base.\u201d\n\n\n\n </Execution Rules>",
                                "temperature": 0.1,
                                "temperatureEnabled": false,
                                "tools": [
                                    {
                                        "component_name": "Retrieval",
                                        "name": "Retrieval",
                                        "params": {
                                            "cross_languages": [],
                                            "description": "A knowledge base of research reports on stock analysis by senior experts",
                                            "empty_response": "",
                                            "kb_ids": [
                                                "60c53ed89acc11f0bc1e7a2a6d0b2755"
                                            ],
                                            "keywords_similarity_weight": 0.7,
                                            "outputs": {
                                                "formalized_content": {
                                                    "type": "string",
                                                    "value": ""
                                                }
                                            },
                                            "rerank_id": "",
                                            "similarity_threshold": 0.2,
                                            "top_k": 1024,
                                            "top_n": 8,
                                            "use_kg": false
                                        }
                                    }
                                ],
                                "topPEnabled": false,
                                "top_p": 0.3,
                                "user_prompt": "This is the order you need to send to the agent.",
                                "visual_files_var": ""
                            },
                            "label": "Agent",
                            "name": "Internal Research Report Retrieval Agent"
                        },
                        "dragging": false,
                        "id": "Agent:MightyIdeasGlow",
                        "measured": {
                            "height": 76,
                            "width": 200
                        },
                        "position": {
                            "x": 787.966928431608,
                            "y": 270.12089782504677
                        },
                        "selected": false,
                        "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."
                            },
                            "label": "Tool",
                            "name": "flow.tool_1"
                        },
                        "dragging": false,
                        "id": "Tool:FullIconsStop",
                        "measured": {
                            "height": 48,
                            "width": 200
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                                "description": "This is an agent for a specific task.",
                                "user_prompt": "This is the order you need to send to the agent."
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                            "label": "Tool",
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