task_id stringlengths 11 16 | level stringclasses 4 values | prompt stringlengths 17 253 | available_tools listlengths 4 8 | expected_trace stringlengths 173 2.74k | expected_final_answer stringlengths 18 1.74k | num_steps int32 1 6 | num_tools_offered int32 4 8 | category stringclasses 9 values | pattern stringlengths 0 29 |
|---|---|---|---|---|---|---|---|---|---|
L1_chain_0101 | L1_chain | Make an invoice for Spark Solutions, then email the details to victor@infra.cloud. | [
"create_invoice",
"send_email",
"create_contact",
"get_session_context",
"store_memory"
] | {"steps": [{"step_id": "step_1", "tool_name": "create_invoice", "arguments": {"client_name": "Spark Solutions", "items": [{"description": "API Integration", "quantity": 1, "unit_price": 3500.0}], "currency": "GBP"}, "depends_on": [], "output_key": "invoice"}, {"step_id": "step_2", "tool_name": "send_email", "arguments": {"to": "victor@infra.cloud", "subject": "Invoice for Spark Solutions", "body": "Invoice inv_5d2a340889ad for Spark Solutions: 3500.0 GBP"}, "depends_on": ["step_1"], "output_key": null}], "final_answer_source": "step_2"} | {"status": "sent", "message_id": "msg_065e2208711e", "to": "victor@infra.cloud", "subject": "Invoice for Spark Solutions", "body_preview": "Invoice inv_5d2a340889ad for Spark Solutions: 3500.0 GBP", "cc": null, "timestamp": "2026-02-22T12:00:00"} | 2 | 5 | chain | create-send |
L1_chain_0102 | L1_chain | Make an invoice for Blue Horizon, then email the details to xander@devops.ci. | [
"create_invoice",
"send_email",
"clamp_value",
"slugify",
"create_calendar_event"
] | {"steps": [{"step_id": "step_1", "tool_name": "create_invoice", "arguments": {"client_name": "Blue Horizon", "items": [{"description": "Data Analysis", "quantity": 8, "unit_price": 175.0}], "currency": "GBP"}, "depends_on": [], "output_key": "invoice"}, {"step_id": "step_2", "tool_name": "send_email", "arguments": {"to": "xander@devops.ci", "subject": "Invoice for Blue Horizon", "body": "Invoice inv_dbc068bd56ed for Blue Horizon: 1400.0 GBP"}, "depends_on": ["step_1"], "output_key": null}], "final_answer_source": "step_2"} | {"status": "sent", "message_id": "msg_fcc77df300e8", "to": "xander@devops.ci", "subject": "Invoice for Blue Horizon", "body_preview": "Invoice inv_dbc068bd56ed for Blue Horizon: 1400.0 GBP", "cc": null, "timestamp": "2026-02-22T12:00:00"} | 2 | 5 | chain | create-send |
L1_chain_0103 | L1_chain | Create an invoice for NextGen Labs and email it to iris@media.news. | [
"create_invoice",
"send_email",
"join_texts",
"get_directions",
"normalize_data"
] | {"steps": [{"step_id": "step_1", "tool_name": "create_invoice", "arguments": {"client_name": "NextGen Labs", "items": [{"description": "API Integration", "quantity": 1, "unit_price": 3500.0}], "currency": "USD"}, "depends_on": [], "output_key": "invoice"}, {"step_id": "step_2", "tool_name": "send_email", "arguments": {"to": "iris@media.news", "subject": "Invoice for NextGen Labs", "body": "Invoice inv_0c5b4cc02f30 for NextGen Labs: 3500.0 USD"}, "depends_on": ["step_1"], "output_key": null}], "final_answer_source": "step_2"} | {"status": "sent", "message_id": "msg_d460c69afef1", "to": "iris@media.news", "subject": "Invoice for NextGen Labs", "body_preview": "Invoice inv_0c5b4cc02f30 for NextGen Labs: 3500.0 USD", "cc": null, "timestamp": "2026-02-22T12:00:00"} | 2 | 5 | chain | create-send |
L1_chain_0104 | L1_chain | Create a "System Health Dashboard" report and write it to the file at /data/system_health_dashboard.txt. | [
"generate_report",
"write_file",
"http_request",
"create_invoice",
"lookup_entity"
] | {"steps": [{"step_id": "step_1", "tool_name": "generate_report", "arguments": {"title": "System Health Dashboard", "data": "{\"revenue\": 125000, \"expenses\": 98000, \"profit\": 27000}", "format": "text"}, "depends_on": [], "output_key": "report"}, {"step_id": "step_2", "tool_name": "write_file", "arguments": {"path": "/data/system_health_dashboard.txt", "content": "SYSTEM HEALTH DASHBOARD\n=======================\n\nSummary:\n revenue: 125000\n expenses: 98000\n profit: 27000\n\nGenerated: 2026-02-22T12:00:00"}, "depends_on": ["step_1"], "output_key": null}], "final_answer_source": "step_2"} | {"status": "written", "path": "/data/system_health_dashboard.txt", "size": 141, "type": "text/plain", "overwrote_existing": false} | 2 | 5 | chain | generate-save |
L1_chain_0105 | L1_chain | Build a report called "Performance Metrics", then save the output to /data/performance_metrics.txt. | [
"generate_report",
"write_file",
"transform_format",
"generate_image",
"get_stock_price"
] | {"steps": [{"step_id": "step_1", "tool_name": "generate_report", "arguments": {"title": "Performance Metrics", "data": "{\"cpu_usage\": 72.5, \"memory_usage\": 68.3, \"disk_usage\": 45.1}", "format": "text"}, "depends_on": [], "output_key": "report"}, {"step_id": "step_2", "tool_name": "write_file", "arguments": {"path": "/data/performance_metrics.txt", "content": "PERFORMANCE METRICS\n===================\n\nSummary:\n cpu_usage: 72.5\n memory_usage: 68.3\n disk_usage: 45.1\n\nGenerated: 2026-02-22T12:00:00"}, "depends_on": ["step_1"], "output_key": null}], "final_answer_source": "step_2"} | {"status": "written", "path": "/data/performance_metrics.txt", "size": 139, "type": "text/plain", "overwrote_existing": false} | 2 | 5 | chain | generate-save |
L1_chain_0106 | L1_chain | Build a report called "Performance Metrics", then save the output to /data/performance_metrics.txt. | [
"generate_report",
"write_file",
"calculator",
"unit_convert",
"get_session_context"
] | {"steps": [{"step_id": "step_1", "tool_name": "generate_report", "arguments": {"title": "Performance Metrics", "data": "{\"sales\": 15000, \"returns\": 320, \"customers\": 1250}", "format": "text"}, "depends_on": [], "output_key": "report"}, {"step_id": "step_2", "tool_name": "write_file", "arguments": {"path": "/data/performance_metrics.txt", "content": "PERFORMANCE METRICS\n===================\n\nSummary:\n sales: 15000\n returns: 320\n customers: 1250\n\nGenerated: 2026-02-22T12:00:00"}, "depends_on": ["step_1"], "output_key": null}], "final_answer_source": "step_2"} | {"status": "written", "path": "/data/performance_metrics.txt", "size": 129, "type": "text/plain", "overwrote_existing": true} | 2 | 5 | chain | generate-save |
L1_chain_0107 | L1_chain | Apply aes256 encryption to "Cybersecurity threats are becoming more sophistica..." and save the output to /data/encrypted_753.enc. | [
"encrypt_text",
"write_file",
"add_duration",
"send_message",
"sentiment_analysis"
] | {"steps": [{"step_id": "step_1", "tool_name": "encrypt_text", "arguments": {"text": "Cybersecurity threats are becoming more sophisticated. Organizations must invest in robust security infrastructure and training.", "method": "aes256"}, "depends_on": [], "output_key": "encrypted"}, {"step_id": "step_2", "tool_name": "write_file", "arguments": {"path": "/data/encrypted_753.enc", "content": "0mrHPS/PYlyAszIthitZYMn4hzFmPlgM2gE7+wqAWNo="}, "depends_on": ["step_1"], "output_key": null}], "final_answer_source": "step_2"} | {"status": "written", "path": "/data/encrypted_753.enc", "size": 44, "type": "text/plain", "overwrote_existing": false} | 2 | 5 | chain | transform-save |
L1_chain_0108 | L1_chain | Apply rsa encryption to "Cybersecurity threats are becoming more sophistica..." and save the output to /data/encrypted_590.enc. | [
"encrypt_text",
"write_file",
"percentile",
"get_location_info",
"unit_convert"
] | {"steps": [{"step_id": "step_1", "tool_name": "encrypt_text", "arguments": {"text": "Cybersecurity threats are becoming more sophisticated. Organizations must invest in robust security infrastructure and training.", "method": "rsa"}, "depends_on": [], "output_key": "encrypted"}, {"step_id": "step_2", "tool_name": "write_file", "arguments": {"path": "/data/encrypted_590.enc", "content": "PnNkVxln1mkjZ0BTzICh7uCaTMHHO+jB62JbYfFHDvE="}, "depends_on": ["step_1"], "output_key": null}], "final_answer_source": "step_2"} | {"status": "written", "path": "/data/encrypted_590.enc", "size": 44, "type": "text/plain", "overwrote_existing": false} | 2 | 5 | chain | transform-save |
L1_chain_0109 | L1_chain | Encrypt the text "Renewable energy adoption is accelerating globally..." using rsa and save the result to /data/encrypted_216.enc. | [
"encrypt_text",
"write_file",
"text_similarity",
"generate_report",
"execute_python"
] | {"steps": [{"step_id": "step_1", "tool_name": "encrypt_text", "arguments": {"text": "Renewable energy adoption is accelerating globally. Solar and wind power costs have decreased dramatically over the past decade.", "method": "rsa"}, "depends_on": [], "output_key": "encrypted"}, {"step_id": "step_2", "tool_name": "write_file", "arguments": {"path": "/data/encrypted_216.enc", "content": "Nox0QNR4P3jmx+vdJ5F43tfjVmrtlYdZRLXCEkx1i5s="}, "depends_on": ["step_1"], "output_key": null}], "final_answer_source": "step_2"} | {"status": "written", "path": "/data/encrypted_216.enc", "size": 44, "type": "text/plain", "overwrote_existing": false} | 2 | 5 | chain | transform-save |
L1_chain_0110 | L1_chain | Slugify "Cybersecurity Essentials 2026" and generate a full URL using base https://www.example.com. | [
"slugify",
"generate_url",
"business_days_between",
"extract_domain",
"get_session_context"
] | {"steps": [{"step_id": "step_1", "tool_name": "slugify", "arguments": {"text": "Cybersecurity Essentials 2026"}, "depends_on": [], "output_key": "slug"}, {"step_id": "step_2", "tool_name": "generate_url", "arguments": {"base": "https://www.example.com", "path": "/articles/cybersecurity-essentials-2026", "params": "{}"}, "depends_on": ["step_1"], "output_key": null}], "final_answer_source": "step_2"} | {"url": "https://www.example.com/articles/cybersecurity-essentials-2026"} | 2 | 5 | chain | transform-construct |
L1_chain_0111 | L1_chain | Slugify "The Art of Technical Writing" and generate a full URL using base https://blog.company.io. | [
"slugify",
"generate_url",
"get_weekday",
"list_memories",
"extract_links"
] | {"steps": [{"step_id": "step_1", "tool_name": "slugify", "arguments": {"text": "The Art of Technical Writing"}, "depends_on": [], "output_key": "slug"}, {"step_id": "step_2", "tool_name": "generate_url", "arguments": {"base": "https://blog.company.io", "path": "/articles/the-art-of-technical-writing", "params": "{}"}, "depends_on": ["step_1"], "output_key": null}], "final_answer_source": "step_2"} | {"url": "https://blog.company.io/articles/the-art-of-technical-writing"} | 2 | 5 | chain | transform-construct |
L1_chain_0112 | L1_chain | Turn "Cloud Infrastructure Best Practices" into a slug, then build the complete URL from https://news.site.org. | [
"slugify",
"generate_url",
"mask_pii",
"generate_summary_stats",
"round_number"
] | {"steps": [{"step_id": "step_1", "tool_name": "slugify", "arguments": {"text": "Cloud Infrastructure Best Practices"}, "depends_on": [], "output_key": "slug"}, {"step_id": "step_2", "tool_name": "generate_url", "arguments": {"base": "https://news.site.org", "path": "/articles/cloud-infrastructure-best-practices", "params": "{}"}, "depends_on": ["step_1"], "output_key": null}], "final_answer_source": "step_2"} | {"url": "https://news.site.org/articles/cloud-infrastructure-best-practices"} | 2 | 5 | chain | transform-construct |
L2_parallel_0113 | L2_parallel | Get weather for Lisbon, Stockholm, Istanbul simultaneously and compare temperatures. | [
"get_weather",
"calculate_date_diff",
"statistical_analysis",
"convert_timezone"
] | {"steps": [{"step_id": "step_1", "tool_name": "get_weather", "arguments": {"city": "Lisbon"}, "depends_on": [], "output_key": "weather_lisbon"}, {"step_id": "step_2", "tool_name": "get_weather", "arguments": {"city": "Stockholm"}, "depends_on": [], "output_key": "weather_stockholm"}, {"step_id": "step_3", "tool_name": "get_weather", "arguments": {"city": "Istanbul"}, "depends_on": [], "output_key": "weather_istanbul"}], "final_answer_source": "step_2"} | {"warmest_city": "Stockholm", "temperature": 39} | 3 | 4 | parallel | fan-out-compare |
L2_parallel_0114 | L2_parallel | Compare the weather in Delhi, Mumbai, Kuala Lumpur. Which city is warmest? | [
"get_weather",
"regex_match",
"write_file",
"web_page_fetch"
] | {"steps": [{"step_id": "step_1", "tool_name": "get_weather", "arguments": {"city": "Delhi"}, "depends_on": [], "output_key": "weather_delhi"}, {"step_id": "step_2", "tool_name": "get_weather", "arguments": {"city": "Mumbai"}, "depends_on": [], "output_key": "weather_mumbai"}, {"step_id": "step_3", "tool_name": "get_weather", "arguments": {"city": "Kuala Lumpur"}, "depends_on": [], "output_key": "weather_kuala_lumpur"}], "final_answer_source": "step_2"} | {"warmest_city": "Mumbai", "temperature": 25} | 3 | 4 | parallel | fan-out-compare |
L2_parallel_0115 | L2_parallel | Compare the weather in Edinburgh, Berlin, Seoul. Which city is warmest? | [
"get_weather",
"get_location_info",
"list_memories",
"execute_python"
] | {"steps": [{"step_id": "step_1", "tool_name": "get_weather", "arguments": {"city": "Edinburgh"}, "depends_on": [], "output_key": "weather_edinburgh"}, {"step_id": "step_2", "tool_name": "get_weather", "arguments": {"city": "Berlin"}, "depends_on": [], "output_key": "weather_berlin"}, {"step_id": "step_3", "tool_name": "get_weather", "arguments": {"city": "Seoul"}, "depends_on": [], "output_key": "weather_seoul"}], "final_answer_source": "step_2"} | {"warmest_city": "Berlin", "temperature": 36} | 3 | 4 | parallel | fan-out-compare |
L2_parallel_0116 | L2_parallel | Look up prices for TSLA, AAPL, PYPL and tell me which costs most. | [
"get_stock_price",
"validate_email",
"compare_texts",
"create_contact"
] | {"steps": [{"step_id": "step_1", "tool_name": "get_stock_price", "arguments": {"symbol": "TSLA"}, "depends_on": [], "output_key": "stock_tsla"}, {"step_id": "step_2", "tool_name": "get_stock_price", "arguments": {"symbol": "AAPL"}, "depends_on": [], "output_key": "stock_aapl"}, {"step_id": "step_3", "tool_name": "get_stock_price", "arguments": {"symbol": "PYPL"}, "depends_on": [], "output_key": "stock_pypl"}], "final_answer_source": "step_1"} | {"highest_stock": "TSLA", "price": 238.45} | 3 | 4 | parallel | fan-out-compare |
L2_parallel_0117 | L2_parallel | Look up prices for INTC, GOOGL, NFLX and tell me which costs most. | [
"get_stock_price",
"slugify",
"percentage_change",
"format_number"
] | {"steps": [{"step_id": "step_1", "tool_name": "get_stock_price", "arguments": {"symbol": "INTC"}, "depends_on": [], "output_key": "stock_intc"}, {"step_id": "step_2", "tool_name": "get_stock_price", "arguments": {"symbol": "GOOGL"}, "depends_on": [], "output_key": "stock_googl"}, {"step_id": "step_3", "tool_name": "get_stock_price", "arguments": {"symbol": "NFLX"}, "depends_on": [], "output_key": "stock_nflx"}], "final_answer_source": "step_3"} | {"highest_stock": "NFLX", "price": 399} | 3 | 4 | parallel | fan-out-compare |
L2_parallel_0118 | L2_parallel | Get current prices of BAC, SQ, MSFT and compare them. | [
"get_stock_price",
"transform_format",
"web_page_fetch",
"ip_geolocation"
] | {"steps": [{"step_id": "step_1", "tool_name": "get_stock_price", "arguments": {"symbol": "BAC"}, "depends_on": [], "output_key": "stock_bac"}, {"step_id": "step_2", "tool_name": "get_stock_price", "arguments": {"symbol": "SQ"}, "depends_on": [], "output_key": "stock_sq"}, {"step_id": "step_3", "tool_name": "get_stock_price", "arguments": {"symbol": "MSFT"}, "depends_on": [], "output_key": "stock_msft"}], "final_answer_source": "step_2"} | {"highest_stock": "SQ", "price": 539} | 3 | 4 | parallel | fan-out-compare |
L2_parallel_0119 | L2_parallel | Analyze the sentiment of these texts: 1) "Rude staff and long wait times, very unpleasant visit...." 2) "The quarterly earnings report will be released on Friday...." 3) "Fantastic experience from start to finish!..." | [
"sentiment_analysis",
"base64_encode",
"encode_url",
"get_directions"
] | {"steps": [{"step_id": "step_1", "tool_name": "sentiment_analysis", "arguments": {"text": "Rude staff and long wait times, very unpleasant visit."}, "depends_on": [], "output_key": "sentiment_1"}, {"step_id": "step_2", "tool_name": "sentiment_analysis", "arguments": {"text": "The quarterly earnings report will be released on Friday."}, "depends_on": [], "output_key": "sentiment_2"}, {"step_id": "step_3", "tool_name": "sentiment_analysis", "arguments": {"text": "Fantastic experience from start to finish!"}, "depends_on": [], "output_key": "sentiment_3"}], "final_answer_source": "step_1"} | [{"text_length": 54, "sentiment": "neutral", "confidence": 0.5, "positive_signals": 0, "negative_signals": 0}, {"text_length": 57, "sentiment": "neutral", "confidence": 0.5, "positive_signals": 0, "negative_signals": 0}, {"text_length": 42, "sentiment": "positive", "confidence": 1.0, "positive_signals": 1, "negative_signals": 0}] | 3 | 4 | parallel | fan-out-analyze |
L2_parallel_0120 | L2_parallel | Analyze the sentiment of these texts: 1) "Overpriced and underperforming, would not recommend...." 2) "Great quality and fast shipping, highly recommend!..." 3) "Please submit your expense reports by end of month...." | [
"sentiment_analysis",
"write_file",
"url_parse",
"business_days_between"
] | {"steps": [{"step_id": "step_1", "tool_name": "sentiment_analysis", "arguments": {"text": "Overpriced and underperforming, would not recommend."}, "depends_on": [], "output_key": "sentiment_1"}, {"step_id": "step_2", "tool_name": "sentiment_analysis", "arguments": {"text": "Great quality and fast shipping, highly recommend!"}, "depends_on": [], "output_key": "sentiment_2"}, {"step_id": "step_3", "tool_name": "sentiment_analysis", "arguments": {"text": "Please submit your expense reports by end of month."}, "depends_on": [], "output_key": "sentiment_3"}], "final_answer_source": "step_1"} | [{"text_length": 52, "sentiment": "positive", "confidence": 1.0, "positive_signals": 1, "negative_signals": 0}, {"text_length": 50, "sentiment": "positive", "confidence": 1.0, "positive_signals": 2, "negative_signals": 0}, {"text_length": 51, "sentiment": "neutral", "confidence": 0.5, "positive_signals": 0, "negative_signals": 0}] | 3 | 4 | parallel | fan-out-analyze |
L2_parallel_0121 | L2_parallel | Analyze the sentiment of these texts: 1) "The parking lot will be repaved starting next Monday...." 2) "The report contains data from Q1 through Q3 of this year...." 3) "The next team meeting will be held in conference room B...." | [
"sentiment_analysis",
"next_occurrence",
"execute_python",
"statistical_analysis"
] | {"steps": [{"step_id": "step_1", "tool_name": "sentiment_analysis", "arguments": {"text": "The parking lot will be repaved starting next Monday."}, "depends_on": [], "output_key": "sentiment_1"}, {"step_id": "step_2", "tool_name": "sentiment_analysis", "arguments": {"text": "The report contains data from Q1 through Q3 of this year."}, "depends_on": [], "output_key": "sentiment_2"}, {"step_id": "step_3", "tool_name": "sentiment_analysis", "arguments": {"text": "The next team meeting will be held in conference room B."}, "depends_on": [], "output_key": "sentiment_3"}], "final_answer_source": "step_1"} | [{"text_length": 53, "sentiment": "neutral", "confidence": 0.5, "positive_signals": 0, "negative_signals": 0}, {"text_length": 57, "sentiment": "neutral", "confidence": 0.5, "positive_signals": 0, "negative_signals": 0}, {"text_length": 56, "sentiment": "neutral", "confidence": 0.5, "positive_signals": 0, "negative_signals": 0}] | 3 | 4 | parallel | fan-out-analyze |
L2_parallel_0122 | L2_parallel | Simultaneously search for "quantum computing applications" and find "How does DNA replication work?" in the KB. | [
"web_search",
"knowledge_base_query",
"get_current_time",
"parse_html",
"extract_entities"
] | {"steps": [{"step_id": "step_1", "tool_name": "web_search", "arguments": {"query": "quantum computing applications"}, "depends_on": [], "output_key": "search"}, {"step_id": "step_2", "tool_name": "knowledge_base_query", "arguments": {"query": "How does DNA replication work?"}, "depends_on": [], "output_key": "kb"}], "final_answer_source": "step_1"} | {"search_results": {"query": "quantum computing applications", "results": [{"title": "Quantum Computing Applications — News Overview", "url": "https://arxiv.org/article/39551", "snippet": "Latest research findings on quantum computing applications from leading institutions and peer-reviewed publications.", "source": "News"}, {"title": "Quantum Computing Applications — Academic Analysis", "url": "https://bbc.com/article/74798", "snippet": "Expert guide to quantum computing applications with detailed explanations, examples, and best practices for practitioners.", "source": "Academic"}, {"title": "Quantum Computing Applications — Forum Guide", "url": "https://reuters.com/article/36239", "snippet": "Comparing different approaches to quantum computing applications — strengths, limitations, and real-world performance.", "source": "Forum"}, {"title": "Quantum Computing Applications — Forum Discussion", "url": "https://medium.com/article/50324", "snippet": "Comparing different approaches to quantum computing applications — strengths, limitations, and real-world performance.", "source": "Forum"}, {"title": "Quantum Computing Applications — Wikipedia Report", "url": "https://en.wikipedia.org/article/71340", "snippet": "A comprehensive overview of quantum computing applications covering key concepts, recent developments, and practical applications.", "source": "Wikipedia"}], "total": 5}, "kb_answer": {"query": "How does DNA replication work?", "domain": "general", "answer": "Based on available knowledge, How does DNA replication work? relates to a topic with multiple facets. Further research may be needed for a complete answer.", "confidence": 0.41, "source": "General Knowledge Base", "found": true}} | 2 | 5 | parallel | independent-merge |
L2_parallel_0123 | L2_parallel | Simultaneously search for "vertical farming techniques" and find "What year was the internet invented?" in the KB. | [
"web_search",
"knowledge_base_query",
"get_weekday",
"create_contact",
"format_number"
] | {"steps": [{"step_id": "step_1", "tool_name": "web_search", "arguments": {"query": "vertical farming techniques"}, "depends_on": [], "output_key": "search"}, {"step_id": "step_2", "tool_name": "knowledge_base_query", "arguments": {"query": "What year was the internet invented?"}, "depends_on": [], "output_key": "kb"}], "final_answer_source": "step_1"} | {"search_results": {"query": "vertical farming techniques", "results": [{"title": "Vertical Farming Techniques — Academic Overview", "url": "https://bbc.com/article/45678", "snippet": "Expert guide to vertical farming techniques with detailed explanations, examples, and best practices for practitioners.", "source": "Academic"}, {"title": "Vertical Farming Techniques — Academic Analysis", "url": "https://sciencedirect.com/article/59393", "snippet": "Expert guide to vertical farming techniques with detailed explanations, examples, and best practices for practitioners.", "source": "Academic"}, {"title": "Vertical Farming Techniques — News Guide", "url": "https://stackoverflow.com/article/70376", "snippet": "Latest research findings on vertical farming techniques from leading institutions and peer-reviewed publications.", "source": "News"}, {"title": "Vertical Farming Techniques — Wikipedia Discussion", "url": "https://en.wikipedia.org/article/42510", "snippet": "A comprehensive overview of vertical farming techniques covering key concepts, recent developments, and practical applications.", "source": "Wikipedia"}, {"title": "Vertical Farming Techniques — Academic Report", "url": "https://sciencedirect.com/article/58743", "snippet": "Expert guide to vertical farming techniques with detailed explanations, examples, and best practices for practitioners.", "source": "Academic"}], "total": 5}, "kb_answer": {"query": "What year was the internet invented?", "domain": "general", "answer": "Based on available knowledge, What year was the internet invented? relates to a topic with multiple facets. Further research may be needed for a complete answer.", "confidence": 0.4, "source": "General Knowledge Base", "found": true}} | 2 | 5 | parallel | independent-merge |
L2_parallel_0124 | L2_parallel | Simultaneously search for "cybersecurity best practices" and find "What is the deepest ocean trench?" in the KB. | [
"web_search",
"knowledge_base_query",
"lookup_entity",
"create_spreadsheet",
"list_memories"
] | {"steps": [{"step_id": "step_1", "tool_name": "web_search", "arguments": {"query": "cybersecurity best practices"}, "depends_on": [], "output_key": "search"}, {"step_id": "step_2", "tool_name": "knowledge_base_query", "arguments": {"query": "What is the deepest ocean trench?"}, "depends_on": [], "output_key": "kb"}], "final_answer_source": "step_1"} | {"search_results": {"query": "cybersecurity best practices", "results": [{"title": "Cybersecurity Best Practices — Blog Overview", "url": "https://nature.com/article/2112", "snippet": "cybersecurity best practices: analysis of current trends, methodologies, and future directions in the field.", "source": "Blog"}, {"title": "Cybersecurity Best Practices — Blog Analysis", "url": "https://nature.com/article/8162", "snippet": "cybersecurity best practices: analysis of current trends, methodologies, and future directions in the field.", "source": "Blog"}, {"title": "Cybersecurity Best Practices — Academic Guide", "url": "https://sciencedirect.com/article/40953", "snippet": "Expert guide to cybersecurity best practices with detailed explanations, examples, and best practices for practitioners.", "source": "Academic"}, {"title": "Cybersecurity Best Practices — Academic Discussion", "url": "https://bbc.com/article/32798", "snippet": "Expert guide to cybersecurity best practices with detailed explanations, examples, and best practices for practitioners.", "source": "Academic"}, {"title": "Cybersecurity Best Practices — Wikipedia Report", "url": "https://en.wikipedia.org/article/78310", "snippet": "A comprehensive overview of cybersecurity best practices covering key concepts, recent developments, and practical applications.", "source": "Wikipedia"}], "total": 5}, "kb_answer": {"query": "What is the deepest ocean trench?", "domain": "general", "answer": "Based on available knowledge, What is the deepest ocean trench? relates to a topic with multiple facets. Further research may be needed for a complete answer.", "confidence": 0.64, "source": "General Knowledge Base", "found": true}} | 2 | 5 | parallel | independent-merge |
L2_parallel_0125 | L2_parallel | Hash the text "The global economy is undergoing a period of significant tra..." using sha256, md5, and sha512 simultaneously. | [
"hash_text",
"base64_decode",
"summarize_text",
"sentiment_analysis"
] | {"steps": [{"step_id": "step_1", "tool_name": "hash_text", "arguments": {"text": "The global economy is undergoing a period of significant transformation. Digital technologies are reshaping traditional business models.", "algorithm": "sha256"}, "depends_on": [], "output_key": "hash_sha256"}, {"step_id": "step_2", "tool_name": "hash_text", "arguments": {"text": "The global economy is undergoing a period of significant transformation. Digital technologies are reshaping traditional business models.", "algorithm": "md5"}, "depends_on": [], "output_key": "hash_md5"}, {"step_id": "step_3", "tool_name": "hash_text", "arguments": {"text": "The global economy is undergoing a period of significant transformation. Digital technologies are reshaping traditional business models.", "algorithm": "sha512"}, "depends_on": [], "output_key": "hash_sha512"}], "final_answer_source": "step_1"} | {"sha256": {"hash": "d8162bb5be5346662286285b590cf03b9186f1441fefb0acd7a05391c3193827", "algorithm": "sha256"}, "md5": {"hash": "b450f107966a87400de4ceb277024fa8", "algorithm": "md5"}, "sha512": {"hash": "a399c26195b2a3899d62c73d16cdb0bec05b6a197a84134ea4041b67eb66db03924d30de4eb01c869acc391c19130da36dbe28eef7fb8e32e635460b9a61c89b", "algorithm": "sha512"}} | 3 | 4 | parallel | fan-out-hash |
L2_parallel_0126 | L2_parallel | Generate all three hashes (SHA-256, MD5, SHA-512) for: "Artificial intelligence has made remarkable progress in rece..." | [
"hash_text",
"summarize_text",
"calculator",
"extract_links"
] | {"steps": [{"step_id": "step_1", "tool_name": "hash_text", "arguments": {"text": "Artificial intelligence has made remarkable progress in recent years. Machine learning models can now perform complex tasks that were once thought impossible.", "algorithm": "sha256"}, "depends_on": [], "output_key": "hash_sha256"}, {"step_id": "step_2", "tool_name": "hash_text", "arguments": {"text": "Artificial intelligence has made remarkable progress in recent years. Machine learning models can now perform complex tasks that were once thought impossible.", "algorithm": "md5"}, "depends_on": [], "output_key": "hash_md5"}, {"step_id": "step_3", "tool_name": "hash_text", "arguments": {"text": "Artificial intelligence has made remarkable progress in recent years. Machine learning models can now perform complex tasks that were once thought impossible.", "algorithm": "sha512"}, "depends_on": [], "output_key": "hash_sha512"}], "final_answer_source": "step_1"} | {"sha256": {"hash": "ad04cbd772f24a471bef86e3163442c46760eea9c429ec7018e7ced62165be85", "algorithm": "sha256"}, "md5": {"hash": "9f050aa18db80159f7cb909d4e6d912f", "algorithm": "md5"}, "sha512": {"hash": "78b662d103429340dbe1eb3b3cdc1bc502f388a142cedb20f58445b7c5267bcb6a0027c9567bf744d109a60c82bfad53626e81c5cad2d55e5491535ef7e44177", "algorithm": "sha512"}} | 3 | 4 | parallel | fan-out-hash |
L2_parallel_0127 | L2_parallel | Hash the text "The future of transportation includes hyperloop systems and ..." using sha256, md5, and sha512 simultaneously. | [
"hash_text",
"set_reminder",
"create_spreadsheet",
"url_parse"
] | {"steps": [{"step_id": "step_1", "tool_name": "hash_text", "arguments": {"text": "The future of transportation includes hyperloop systems and urban air mobility. These technologies aim to reduce travel times significantly.", "algorithm": "sha256"}, "depends_on": [], "output_key": "hash_sha256"}, {"step_id": "step_2", "tool_name": "hash_text", "arguments": {"text": "The future of transportation includes hyperloop systems and urban air mobility. These technologies aim to reduce travel times significantly.", "algorithm": "md5"}, "depends_on": [], "output_key": "hash_md5"}, {"step_id": "step_3", "tool_name": "hash_text", "arguments": {"text": "The future of transportation includes hyperloop systems and urban air mobility. These technologies aim to reduce travel times significantly.", "algorithm": "sha512"}, "depends_on": [], "output_key": "hash_sha512"}], "final_answer_source": "step_1"} | {"sha256": {"hash": "36609d114a61067a13e8a2a7369d97e01bf0a20e7cf8e5eea0974b7fa2109c44", "algorithm": "sha256"}, "md5": {"hash": "098f59b77ea50480cbed8694b7035024", "algorithm": "md5"}, "sha512": {"hash": "920c28253ecc7b71d7e9ad2492bf7e70177bec29ed1aae22ffcfaece192aa01a0942baf5c3389bf18710acd043f8238affd09bbf7ccb82f41b880b3d6fc5c244", "algorithm": "sha512"}} | 3 | 4 | parallel | fan-out-hash |
L2_parallel_0128 | L2_parallel | Get the value of 2185.07 NZD in each of INR, CAD, THB. | [
"get_exchange_rate",
"data_filter",
"create_contact",
"standard_deviation"
] | {"steps": [{"step_id": "step_1", "tool_name": "get_exchange_rate", "arguments": {"from_currency": "NZD", "to_currency": "INR", "amount": 2185.07}, "depends_on": [], "output_key": "exchange_inr"}, {"step_id": "step_2", "tool_name": "get_exchange_rate", "arguments": {"from_currency": "NZD", "to_currency": "CAD", "amount": 2185.07}, "depends_on": [], "output_key": "exchange_cad"}, {"step_id": "step_3", "tool_name": "get_exchange_rate", "arguments": {"from_currency": "NZD", "to_currency": "THB", "amount": 2185.07}, "depends_on": [], "output_key": "exchange_thb"}], "final_answer_source": "step_1"} | {"INR": {"from_currency": "NZD", "to_currency": "INR", "rate": 83.12, "amount": 2185.07, "converted_amount": 181623.0184}, "CAD": {"from_currency": "NZD", "to_currency": "CAD", "rate": 1.36, "amount": 2185.07, "converted_amount": 2971.6952}, "THB": {"from_currency": "NZD", "to_currency": "THB", "rate": 1.0, "amount": 2185.07, "converted_amount": 2185.07}} | 3 | 4 | parallel | fan-out-exchange |
L2_parallel_0129 | L2_parallel | Exchange 4290.09 HKD into BRL, RUB, JPY at the same time. | [
"get_exchange_rate",
"keyword_extract",
"validate_email",
"send_webhook"
] | {"steps": [{"step_id": "step_1", "tool_name": "get_exchange_rate", "arguments": {"from_currency": "HKD", "to_currency": "BRL", "amount": 4290.09}, "depends_on": [], "output_key": "exchange_brl"}, {"step_id": "step_2", "tool_name": "get_exchange_rate", "arguments": {"from_currency": "HKD", "to_currency": "RUB", "amount": 4290.09}, "depends_on": [], "output_key": "exchange_rub"}, {"step_id": "step_3", "tool_name": "get_exchange_rate", "arguments": {"from_currency": "HKD", "to_currency": "JPY", "amount": 4290.09}, "depends_on": [], "output_key": "exchange_jpy"}], "final_answer_source": "step_1"} | {"BRL": {"from_currency": "HKD", "to_currency": "BRL", "rate": 0.63555, "amount": 4290.09, "converted_amount": 2726.5662}, "RUB": {"from_currency": "HKD", "to_currency": "RUB", "rate": 0.127877, "amount": 4290.09, "converted_amount": 548.6049}, "JPY": {"from_currency": "HKD", "to_currency": "JPY", "rate": 19.117647, "amount": 4290.09, "converted_amount": 82016.4265}} | 3 | 4 | parallel | fan-out-exchange |
L2_parallel_0130 | L2_parallel | Convert 193.12 KRW to RUB, HKD, PLN simultaneously. | [
"get_exchange_rate",
"normalize_data",
"database_query",
"correlation"
] | {"steps": [{"step_id": "step_1", "tool_name": "get_exchange_rate", "arguments": {"from_currency": "KRW", "to_currency": "RUB", "amount": 193.12}, "depends_on": [], "output_key": "exchange_rub"}, {"step_id": "step_2", "tool_name": "get_exchange_rate", "arguments": {"from_currency": "KRW", "to_currency": "HKD", "amount": 193.12}, "depends_on": [], "output_key": "exchange_hkd"}, {"step_id": "step_3", "tool_name": "get_exchange_rate", "arguments": {"from_currency": "KRW", "to_currency": "PLN", "amount": 193.12}, "depends_on": [], "output_key": "exchange_pln"}], "final_answer_source": "step_1"} | {"RUB": {"from_currency": "KRW", "to_currency": "RUB", "rate": 0.000754, "amount": 193.12, "converted_amount": 0.1457}, "HKD": {"from_currency": "KRW", "to_currency": "HKD", "rate": 0.0059, "amount": 193.12, "converted_amount": 1.1393}, "PLN": {"from_currency": "KRW", "to_currency": "PLN", "rate": 0.000754, "amount": 193.12, "converted_amount": 0.1457}} | 3 | 4 | parallel | fan-out-exchange |
L2_parallel_0131 | L2_parallel | Display the date 2025-07-04 in short format, long format, and ISO format at once. | [
"format_date",
"create_spreadsheet",
"send_email",
"base64_encode"
] | {"steps": [{"step_id": "step_1", "tool_name": "format_date", "arguments": {"date": "2025-07-04", "format": "short"}, "depends_on": [], "output_key": "date_short"}, {"step_id": "step_2", "tool_name": "format_date", "arguments": {"date": "2025-07-04", "format": "long"}, "depends_on": [], "output_key": "date_long"}, {"step_id": "step_3", "tool_name": "format_date", "arguments": {"date": "2025-07-04", "format": "iso"}, "depends_on": [], "output_key": "date_iso"}], "final_answer_source": "step_1"} | {"short": {"formatted": "Jul 04, 2025"}, "long": {"formatted": "July 04, 2025"}, "iso": {"formatted": "2025-07-04"}} | 3 | 4 | parallel | fan-out-format |
L2_parallel_0132 | L2_parallel | Display the date 2025-11-15 in short format, long format, and ISO format at once. | [
"format_date",
"keyword_extract",
"unit_convert",
"get_directions"
] | {"steps": [{"step_id": "step_1", "tool_name": "format_date", "arguments": {"date": "2025-11-15", "format": "short"}, "depends_on": [], "output_key": "date_short"}, {"step_id": "step_2", "tool_name": "format_date", "arguments": {"date": "2025-11-15", "format": "long"}, "depends_on": [], "output_key": "date_long"}, {"step_id": "step_3", "tool_name": "format_date", "arguments": {"date": "2025-11-15", "format": "iso"}, "depends_on": [], "output_key": "date_iso"}], "final_answer_source": "step_1"} | {"short": {"formatted": "Nov 15, 2025"}, "long": {"formatted": "November 15, 2025"}, "iso": {"formatted": "2025-11-15"}} | 3 | 4 | parallel | fan-out-format |
L2_parallel_0133 | L2_parallel | Convert 2026-06-01 into short, long, and ISO date representations. | [
"format_date",
"calculate_date_diff",
"parse_html",
"send_webhook"
] | {"steps": [{"step_id": "step_1", "tool_name": "format_date", "arguments": {"date": "2026-06-01", "format": "short"}, "depends_on": [], "output_key": "date_short"}, {"step_id": "step_2", "tool_name": "format_date", "arguments": {"date": "2026-06-01", "format": "long"}, "depends_on": [], "output_key": "date_long"}, {"step_id": "step_3", "tool_name": "format_date", "arguments": {"date": "2026-06-01", "format": "iso"}, "depends_on": [], "output_key": "date_iso"}], "final_answer_source": "step_1"} | {"short": {"formatted": "Jun 01, 2026"}, "long": {"formatted": "June 01, 2026"}, "iso": {"formatted": "2026-06-01"}} | 3 | 4 | parallel | fan-out-format |
L2_parallel_0134 | L2_parallel | Extract keywords from these three texts: 1) "Water scarcity is a growing global concern affecti..." 2) "The gig economy continues to grow, raising questio..." 3) "The future of transportation includes hyperloop sy..." | [
"keyword_extract",
"search_products",
"web_page_fetch",
"list_memories"
] | {"steps": [{"step_id": "step_1", "tool_name": "keyword_extract", "arguments": {"text": "Water scarcity is a growing global concern affecting billions of people. Desalination and water recycling technologies are crucial."}, "depends_on": [], "output_key": "keywords_1"}, {"step_id": "step_2", "tool_name": "keyword_extract", "arguments": {"text": "The gig economy continues to grow, raising questions about worker protections and benefits. Policy makers are debating new frameworks."}, "depends_on": [], "output_key": "keywords_2"}, {"step_id": "step_3", "tool_name": "keyword_extract", "arguments": {"text": "The future of transportation includes hyperloop systems and urban air mobility. These technologies aim to reduce travel times significantly."}, "depends_on": [], "output_key": "keywords_3"}], "final_answer_source": "step_1"} | [{"keywords": ["water", "scarcity", "growing", "global", "concern"], "scores": [1.0, 0.5, 0.5, 0.5, 0.5]}, {"keywords": ["gig", "economy", "continues", "grow", "raising"], "scores": [1.0, 1.0, 1.0, 1.0, 1.0]}, {"keywords": ["future", "transportation", "includes", "hyperloop", "systems"], "scores": [1.0, 1.0, 1.0, 1.0, 1.0]}] | 3 | 4 | parallel | fan-out-keywords |
L2_parallel_0135 | L2_parallel | Find the key terms in each of these texts simultaneously: "The rise of remote work has fundamentally changed ...", "Biodegradable materials are replacing traditional ...", "Cybersecurity threats are becoming more sophistica..." | [
"keyword_extract",
"format_number",
"generate_report",
"get_weekday"
] | {"steps": [{"step_id": "step_1", "tool_name": "keyword_extract", "arguments": {"text": "The rise of remote work has fundamentally changed workplace dynamics. Companies are rethinking office spaces and collaboration tools."}, "depends_on": [], "output_key": "keywords_1"}, {"step_id": "step_2", "tool_name": "keyword_extract", "arguments": {"text": "Biodegradable materials are replacing traditional plastics in packaging. Consumer demand is driving innovation in sustainable materials."}, "depends_on": [], "output_key": "keywords_2"}, {"step_id": "step_3", "tool_name": "keyword_extract", "arguments": {"text": "Cybersecurity threats are becoming more sophisticated. Organizations must invest in robust security infrastructure and training."}, "depends_on": [], "output_key": "keywords_3"}], "final_answer_source": "step_1"} | [{"keywords": ["rise", "remote", "work", "fundamentally", "changed"], "scores": [1.0, 1.0, 1.0, 1.0, 1.0]}, {"keywords": ["materials", "biodegradable", "replacing", "traditional", "plastics"], "scores": [1.0, 0.5, 0.5, 0.5, 0.5]}, {"keywords": ["cybersecurity", "threats", "becoming", "sophisticated", "organizations"], "scores": [1.0, 1.0, 1.0, 1.0, 1.0]}] | 3 | 4 | parallel | fan-out-keywords |
L2_parallel_0136 | L2_parallel | Extract keywords from these three texts: 1) "Artificial intelligence has made remarkable progre..." 2) "Water scarcity is a growing global concern affecti..." 3) "The metaverse concept is evolving from gaming to b..." | [
"keyword_extract",
"slugify",
"get_weather",
"min_max"
] | {"steps": [{"step_id": "step_1", "tool_name": "keyword_extract", "arguments": {"text": "Artificial intelligence has made remarkable progress in recent years. Machine learning models can now perform complex tasks that were once thought impossible."}, "depends_on": [], "output_key": "keywords_1"}, {"step_id": "step_2", "tool_name": "keyword_extract", "arguments": {"text": "Water scarcity is a growing global concern affecting billions of people. Desalination and water recycling technologies are crucial."}, "depends_on": [], "output_key": "keywords_2"}, {"step_id": "step_3", "tool_name": "keyword_extract", "arguments": {"text": "The metaverse concept is evolving from gaming to business applications. Virtual reality meetings and digital workspaces are becoming mainstream."}, "depends_on": [], "output_key": "keywords_3"}], "final_answer_source": "step_1"} | [{"keywords": ["artificial", "intelligence", "made", "remarkable", "progress"], "scores": [1.0, 1.0, 1.0, 1.0, 1.0]}, {"keywords": ["water", "scarcity", "growing", "global", "concern"], "scores": [1.0, 0.5, 0.5, 0.5, 0.5]}, {"keywords": ["metaverse", "concept", "evolving", "gaming", "business"], "scores": [1.0, 1.0, 1.0, 1.0, 1.0]}] | 3 | 4 | parallel | fan-out-keywords |
L2_parallel_0137 | L2_parallel | Detect the language of each text: 1) "Las calles de la ciudad están llenas de gente disf..." 2) "The weather is beautiful today and I feel great." 3) "Kunstmatige intelligentie heeft de manier waarop w..." | [
"detect_language",
"write_file",
"is_business_day",
"set_reminder"
] | {"steps": [{"step_id": "step_1", "tool_name": "detect_language", "arguments": {"text": "Las calles de la ciudad están llenas de gente disfrutando del sol del fin de semana."}, "depends_on": [], "output_key": "lang_1"}, {"step_id": "step_2", "tool_name": "detect_language", "arguments": {"text": "The weather is beautiful today and I feel great."}, "depends_on": [], "output_key": "lang_2"}, {"step_id": "step_3", "tool_name": "detect_language", "arguments": {"text": "Kunstmatige intelligentie heeft de manier waarop we werken en leven gerevolutioneerd."}, "depends_on": [], "output_key": "lang_3"}], "final_answer_source": "step_1"} | [{"language": "Spanish", "language_code": "es", "confidence": 0.44}, {"language": "English", "language_code": "en", "confidence": 0.75}, {"language": "French", "language_code": "fr", "confidence": 0.33}] | 3 | 4 | parallel | fan-out-detect |
L2_parallel_0138 | L2_parallel | Detect the language of each text: 1) "El clima está hermoso hoy y las flores están flore..." 2) "Education is the foundation of a prosperous and eq..." 3) "De kennis van vandaag is de basis van de welvaart ..." | [
"detect_language",
"send_webhook",
"truncate_text",
"percentage_change"
] | {"steps": [{"step_id": "step_1", "tool_name": "detect_language", "arguments": {"text": "El clima está hermoso hoy y las flores están floreciendo."}, "depends_on": [], "output_key": "lang_1"}, {"step_id": "step_2", "tool_name": "detect_language", "arguments": {"text": "Education is the foundation of a prosperous and equitable society."}, "depends_on": [], "output_key": "lang_2"}, {"step_id": "step_3", "tool_name": "detect_language", "arguments": {"text": "De kennis van vandaag is de basis van de welvaart van morgen."}, "depends_on": [], "output_key": "lang_3"}], "final_answer_source": "step_1"} | [{"language": "Spanish", "language_code": "es", "confidence": 1.0}, {"language": "English", "language_code": "en", "confidence": 0.8}, {"language": "Dutch", "language_code": "nl", "confidence": 0.5}] | 3 | 4 | parallel | fan-out-detect |
L2_parallel_0139 | L2_parallel | What languages are these texts written in? "Die Straßen der Stadt sind voll mit Menschen die d...", "Bildung ist die Grundlage einer wohlhabenden und g...", "L'intelligence artificielle a révolutionné notre f..." | [
"detect_language",
"create_invoice",
"create_contact",
"merge_data"
] | {"steps": [{"step_id": "step_1", "tool_name": "detect_language", "arguments": {"text": "Die Straßen der Stadt sind voll mit Menschen die das Wochenende genießen."}, "depends_on": [], "output_key": "lang_1"}, {"step_id": "step_2", "tool_name": "detect_language", "arguments": {"text": "Bildung ist die Grundlage einer wohlhabenden und gerechten Gesellschaft."}, "depends_on": [], "output_key": "lang_2"}, {"step_id": "step_3", "tool_name": "detect_language", "arguments": {"text": "L'intelligence artificielle a révolutionné notre façon de travailler et de vivre."}, "depends_on": [], "output_key": "lang_3"}], "final_answer_source": "step_1"} | [{"language": "German", "language_code": "de", "confidence": 0.8}, {"language": "German", "language_code": "de", "confidence": 1.0}, {"language": "French", "language_code": "fr", "confidence": 0.5}] | 3 | 4 | parallel | fan-out-detect |
L2_parallel_0140 | L2_parallel | Simultaneously check the weather for Copenhagen and look up ABNB's stock price. | [
"get_weather",
"get_stock_price",
"tokenize_text",
"search_products",
"get_current_time"
] | {"steps": [{"step_id": "step_1", "tool_name": "get_weather", "arguments": {"city": "Copenhagen"}, "depends_on": [], "output_key": "weather"}, {"step_id": "step_2", "tool_name": "get_stock_price", "arguments": {"symbol": "ABNB"}, "depends_on": [], "output_key": "stock"}], "final_answer_source": "step_1"} | {"weather": {"city": "Copenhagen", "temperature_celsius": 38, "humidity_percent": 58, "wind_speed_kmh": 18, "condition": "foggy", "feels_like_celsius": 36}, "stock": {"symbol": "ABNB", "name": "ABNB Corp.", "price_usd": 306, "change_usd": -4.4, "change_percent": -1.44, "market_cap": "256B"}} | 2 | 5 | parallel | independent-merge |
L2_parallel_0141 | L2_parallel | Simultaneously check the weather for Prague and look up COIN's stock price. | [
"get_weather",
"get_stock_price",
"data_sort",
"percentage_change",
"dns_lookup"
] | {"steps": [{"step_id": "step_1", "tool_name": "get_weather", "arguments": {"city": "Prague"}, "depends_on": [], "output_key": "weather"}, {"step_id": "step_2", "tool_name": "get_stock_price", "arguments": {"symbol": "COIN"}, "depends_on": [], "output_key": "stock"}], "final_answer_source": "step_1"} | {"weather": {"city": "Prague", "temperature_celsius": 33, "humidity_percent": 83, "wind_speed_kmh": 3, "condition": "foggy", "feels_like_celsius": 31}, "stock": {"symbol": "COIN", "name": "COIN Corp.", "price_usd": 455, "change_usd": 0.5, "change_percent": 0.11, "market_cap": "405B"}} | 2 | 5 | parallel | independent-merge |
L2_parallel_0142 | L2_parallel | In parallel: weather in Johannesburg and stock price for CRM. | [
"get_weather",
"get_stock_price",
"send_email",
"round_number",
"join_texts"
] | {"steps": [{"step_id": "step_1", "tool_name": "get_weather", "arguments": {"city": "Johannesburg"}, "depends_on": [], "output_key": "weather"}, {"step_id": "step_2", "tool_name": "get_stock_price", "arguments": {"symbol": "CRM"}, "depends_on": [], "output_key": "stock"}], "final_answer_source": "step_1"} | {"weather": {"city": "Johannesburg", "temperature_celsius": 5, "humidity_percent": 40, "wind_speed_kmh": 20, "condition": "partly cloudy", "feels_like_celsius": 3}, "stock": {"symbol": "CRM", "name": "CRM Corp.", "price_usd": 439, "change_usd": 8.9, "change_percent": 2.03, "market_cap": "389B"}} | 2 | 5 | parallel | independent-merge |
L2_parallel_0143 | L2_parallel | Get the word count for three texts simultaneously: "Cybersecurity threats are becoming more sophistica...", "3D printing technology is advancing into new mater...", "Gene editing technologies like CRISPR offer enormo..." | [
"word_count",
"json_extract",
"create_invoice",
"business_days_between"
] | {"steps": [{"step_id": "step_1", "tool_name": "word_count", "arguments": {"text": "Cybersecurity threats are becoming more sophisticated. Organizations must invest in robust security infrastructure and training."}, "depends_on": [], "output_key": "wc_1"}, {"step_id": "step_2", "tool_name": "word_count", "arguments": {"text": "3D printing technology is advancing into new materials and applications. From medical implants to housing construction, possibilities expand."}, "depends_on": [], "output_key": "wc_2"}, {"step_id": "step_3", "tool_name": "word_count", "arguments": {"text": "Gene editing technologies like CRISPR offer enormous potential for treating genetic diseases. Ethical considerations remain important."}, "depends_on": [], "output_key": "wc_3"}], "final_answer_source": "step_1"} | [{"words": 15, "characters": 128, "sentences": 2}, {"words": 18, "characters": 141, "sentences": 2}, {"words": 16, "characters": 134, "sentences": 2}] | 3 | 4 | parallel | fan-out-count |
L2_parallel_0144 | L2_parallel | How many words are in each text? "The gig economy continues to grow, raising questio...", "Urban farming and vertical agriculture are address...", "Advancements in battery technology are driving the..." | [
"word_count",
"correlation",
"retrieve_memory",
"extract_links"
] | {"steps": [{"step_id": "step_1", "tool_name": "word_count", "arguments": {"text": "The gig economy continues to grow, raising questions about worker protections and benefits. Policy makers are debating new frameworks."}, "depends_on": [], "output_key": "wc_1"}, {"step_id": "step_2", "tool_name": "word_count", "arguments": {"text": "Urban farming and vertical agriculture are addressing food security in cities. These innovations reduce transportation costs and carbon emissions."}, "depends_on": [], "output_key": "wc_2"}, {"step_id": "step_3", "tool_name": "word_count", "arguments": {"text": "Advancements in battery technology are driving the electric vehicle revolution. Solid-state batteries promise longer range and faster charging."}, "depends_on": [], "output_key": "wc_3"}], "final_answer_source": "step_1"} | [{"words": 19, "characters": 134, "sentences": 2}, {"words": 19, "characters": 146, "sentences": 2}, {"words": 18, "characters": 143, "sentences": 2}] | 3 | 4 | parallel | fan-out-count |
L2_parallel_0145 | L2_parallel | How many words are in each text? "Urban farming and vertical agriculture are address...", "The education sector is embracing technology-enhan...", "The gig economy continues to grow, raising questio..." | [
"word_count",
"translate_text",
"format_number",
"transform_format"
] | {"steps": [{"step_id": "step_1", "tool_name": "word_count", "arguments": {"text": "Urban farming and vertical agriculture are addressing food security in cities. These innovations reduce transportation costs and carbon emissions."}, "depends_on": [], "output_key": "wc_1"}, {"step_id": "step_2", "tool_name": "word_count", "arguments": {"text": "The education sector is embracing technology-enhanced learning. Online platforms and AI tutors are making education more accessible."}, "depends_on": [], "output_key": "wc_2"}, {"step_id": "step_3", "tool_name": "word_count", "arguments": {"text": "The gig economy continues to grow, raising questions about worker protections and benefits. Policy makers are debating new frameworks."}, "depends_on": [], "output_key": "wc_3"}], "final_answer_source": "step_1"} | [{"words": 19, "characters": 146, "sentences": 2}, {"words": 17, "characters": 132, "sentences": 2}, {"words": 19, "characters": 134, "sentences": 2}] | 3 | 4 | parallel | fan-out-count |
L2_parallel_0146 | L2_parallel | Find all numeric values in these three passages: "Quarterly review with VP of Sales Mark Thompson is...", "Send feedback to feedback@startup.co by March 1st....", "The report was authored by Dr. Lisa Wang from Stan..." | [
"extract_numbers",
"calculator",
"moving_average",
"http_request"
] | {"steps": [{"step_id": "step_1", "tool_name": "extract_numbers", "arguments": {"text": "Quarterly review with VP of Sales Mark Thompson is on 02/28/2026 in the NYC office."}, "depends_on": [], "output_key": "numbers_1"}, {"step_id": "step_2", "tool_name": "extract_numbers", "arguments": {"text": "Send feedback to feedback@startup.co by March 1st. We are located in Berlin, Germany."}, "depends_on": [], "output_key": "numbers_2"}, {"step_id": "step_3", "tool_name": "extract_numbers", "arguments": {"text": "The report was authored by Dr. Lisa Wang from Stanford University on 05/10/2025."}, "depends_on": [], "output_key": "numbers_3"}], "final_answer_source": "step_1"} | [{"numbers": [2, 28, 202, 6], "count": 4}, {"numbers": [1], "count": 1}, {"numbers": [5, 10, 202, 5], "count": 4}] | 3 | 4 | parallel | fan-out-extract |
L2_parallel_0147 | L2_parallel | Find all numeric values in these three passages: "Tim Berners-Lee invented the World Wide Web in 198...", "Submit travel requests to travel@corp.org at least...", "Alan Turing published his seminal paper on computa..." | [
"extract_numbers",
"detect_language",
"base64_encode",
"rss_feed_parse"
] | {"steps": [{"step_id": "step_1", "tool_name": "extract_numbers", "arguments": {"text": "Tim Berners-Lee invented the World Wide Web in 1989 at CERN in Geneva, Switzerland."}, "depends_on": [], "output_key": "numbers_1"}, {"step_id": "step_2", "tool_name": "extract_numbers", "arguments": {"text": "Submit travel requests to travel@corp.org at least 14 days before departure."}, "depends_on": [], "output_key": "numbers_2"}, {"step_id": "step_3", "tool_name": "extract_numbers", "arguments": {"text": "Alan Turing published his seminal paper on computability in 1936 at Cambridge."}, "depends_on": [], "output_key": "numbers_3"}], "final_answer_source": "step_1"} | [{"numbers": [198, 9], "count": 2}, {"numbers": [14], "count": 1}, {"numbers": [193, 6], "count": 2}] | 3 | 4 | parallel | fan-out-extract |
L2_parallel_0148 | L2_parallel | Find all numeric values in these three passages: "Final exam schedule: email registrar@university.ed...", "New hire orientation: Contact onboarding@company.c...", "The conference keynote by Prof. James Chen is on J..." | [
"extract_numbers",
"schedule_meeting",
"parse_date",
"log_event"
] | {"steps": [{"step_id": "step_1", "tool_name": "extract_numbers", "arguments": {"text": "Final exam schedule: email registrar@university.edu or visit room 301 in Admin Building."}, "depends_on": [], "output_key": "numbers_1"}, {"step_id": "step_2", "tool_name": "extract_numbers", "arguments": {"text": "New hire orientation: Contact onboarding@company.com before your start date of 03/01/2026."}, "depends_on": [], "output_key": "numbers_2"}, {"step_id": "step_3", "tool_name": "extract_numbers", "arguments": {"text": "The conference keynote by Prof. James Chen is on January 20, 2026 at MIT."}, "depends_on": [], "output_key": "numbers_3"}], "final_answer_source": "step_1"} | [{"numbers": [301], "count": 1}, {"numbers": [3, 1, 202, 6], "count": 4}, {"numbers": [20, 202, 6], "count": 3}] | 3 | 4 | parallel | fan-out-extract |
L2_parallel_0149 | L2_parallel | In parallel: weather in Lisbon and current time in Europe/Lisbon. | [
"get_weather",
"get_current_time",
"format_date",
"mask_pii",
"compress_data"
] | {"steps": [{"step_id": "step_1", "tool_name": "get_weather", "arguments": {"city": "Lisbon"}, "depends_on": [], "output_key": "weather"}, {"step_id": "step_2", "tool_name": "get_current_time", "arguments": {"timezone": "Europe/Lisbon"}, "depends_on": [], "output_key": "time"}], "final_answer_source": "step_1"} | {"weather": {"city": "Lisbon", "temperature_celsius": 16, "humidity_percent": 66, "wind_speed_kmh": 36, "condition": "snowy", "feels_like_celsius": 14}, "current_time": {"datetime": "2026-03-15T14:30:00+00:00", "date": "2026-03-15", "time": "14:30:00", "timezone": "Europe/Lisbon", "day_of_week": "Sunday", "unix_timestamp": 1773585000}} | 2 | 5 | parallel | independent-merge |
L2_parallel_0150 | L2_parallel | Get the weather in Copenhagen and the current time in Europe/Copenhagen simultaneously. | [
"get_weather",
"get_current_time",
"join_texts",
"next_occurrence",
"keyword_extract"
] | {"steps": [{"step_id": "step_1", "tool_name": "get_weather", "arguments": {"city": "Copenhagen"}, "depends_on": [], "output_key": "weather"}, {"step_id": "step_2", "tool_name": "get_current_time", "arguments": {"timezone": "Europe/Copenhagen"}, "depends_on": [], "output_key": "time"}], "final_answer_source": "step_1"} | {"weather": {"city": "Copenhagen", "temperature_celsius": 38, "humidity_percent": 58, "wind_speed_kmh": 18, "condition": "foggy", "feels_like_celsius": 36}, "current_time": {"datetime": "2026-03-15T15:30:00+01:00", "date": "2026-03-15", "time": "15:30:00", "timezone": "Europe/Copenhagen", "day_of_week": "Sunday", "unix_timestamp": 1773585000}} | 2 | 5 | parallel | independent-merge |
L2_parallel_0151 | L2_parallel | Check both the weather for Dublin and the current time in Europe/Dublin. | [
"get_weather",
"get_current_time",
"execute_python",
"create_contact",
"rss_feed_parse"
] | {"steps": [{"step_id": "step_1", "tool_name": "get_weather", "arguments": {"city": "Dublin"}, "depends_on": [], "output_key": "weather"}, {"step_id": "step_2", "tool_name": "get_current_time", "arguments": {"timezone": "Europe/Dublin"}, "depends_on": [], "output_key": "time"}], "final_answer_source": "step_1"} | {"weather": {"city": "Dublin", "temperature_celsius": 11, "humidity_percent": 46, "wind_speed_kmh": 6, "condition": "snowy", "feels_like_celsius": 9}, "current_time": {"datetime": "2026-03-15T14:30:00+00:00", "date": "2026-03-15", "time": "14:30:00", "timezone": "Europe/Dublin", "day_of_week": "Sunday", "unix_timestamp": 1773585000}} | 2 | 5 | parallel | independent-merge |
L2_parallel_0152 | L2_parallel | Count business days for three date ranges simultaneously: 2026-04-01 to 2026-10-31, 2026-01-15 to 2026-07-15, 2025-10-01 to 2026-10-01. | [
"business_days_between",
"tokenize_text",
"json_extract",
"data_aggregate"
] | {"steps": [{"step_id": "step_1", "tool_name": "business_days_between", "arguments": {"start_date": "2026-04-01", "end_date": "2026-10-31"}, "depends_on": [], "output_key": "bdays_1"}, {"step_id": "step_2", "tool_name": "business_days_between", "arguments": {"start_date": "2026-01-15", "end_date": "2026-07-15"}, "depends_on": [], "output_key": "bdays_2"}, {"step_id": "step_3", "tool_name": "business_days_between", "arguments": {"start_date": "2025-10-01", "end_date": "2026-10-01"}, "depends_on": [], "output_key": "bdays_3"}], "final_answer_source": "step_1"} | {"2026-04-01_to_2026-10-31": {"business_days": 152, "total_days": 213}, "2026-01-15_to_2026-07-15": {"business_days": 129, "total_days": 181}, "2025-10-01_to_2026-10-01": {"business_days": 261, "total_days": 365}} | 3 | 4 | parallel | fan-out-business-days |
L3_dag_0153 | L3_dag | Get weather for Jakarta and Montreal, find which is warmer, convert to °F, then email rachel@product.mgmt. | [
"get_weather",
"unit_convert",
"send_email",
"linear_regression",
"url_parse",
"join_texts"
] | {"steps": [{"step_id": "step_1", "tool_name": "get_weather", "arguments": {"city": "Jakarta"}, "depends_on": [], "output_key": "w1"}, {"step_id": "step_2", "tool_name": "get_weather", "arguments": {"city": "Montreal"}, "depends_on": [], "output_key": "w2"}, {"step_id": "step_3", "tool_name": "unit_convert", "arguments": {"value": 33, "from_unit": "celsius", "to_unit": "fahrenheit"}, "depends_on": ["step_1", "step_2"], "output_key": null}, {"step_id": "step_4", "tool_name": "send_email", "arguments": {"to": "rachel@product.mgmt", "subject": "Weather Comparison", "body": "The warmer city is Jakarta at 91.4°F"}, "depends_on": ["step_3"], "output_key": null}], "final_answer_source": "step_4"} | {"status": "sent", "message_id": "msg_d2bc9390f8e3", "to": "rachel@product.mgmt", "subject": "Weather Comparison", "body_preview": "The warmer city is Jakarta at 91.4°F", "cc": null, "timestamp": "2026-02-22T12:00:00"} | 4 | 6 | dag | parallel-merge-chain |
L3_dag_0154 | L3_dag | Compare weather in Kuala Lumpur and Warsaw, convert the warmer temp to Fahrenheit, and email the result to rachel@product.mgmt. | [
"get_weather",
"unit_convert",
"send_email",
"string_replace",
"data_aggregate",
"database_query"
] | {"steps": [{"step_id": "step_1", "tool_name": "get_weather", "arguments": {"city": "Kuala Lumpur"}, "depends_on": [], "output_key": "w1"}, {"step_id": "step_2", "tool_name": "get_weather", "arguments": {"city": "Warsaw"}, "depends_on": [], "output_key": "w2"}, {"step_id": "step_3", "tool_name": "unit_convert", "arguments": {"value": 35, "from_unit": "celsius", "to_unit": "fahrenheit"}, "depends_on": ["step_1", "step_2"], "output_key": null}, {"step_id": "step_4", "tool_name": "send_email", "arguments": {"to": "rachel@product.mgmt", "subject": "Weather Comparison", "body": "The warmer city is Warsaw at 95.0°F"}, "depends_on": ["step_3"], "output_key": null}], "final_answer_source": "step_4"} | {"status": "sent", "message_id": "msg_d2bc9390f8e3", "to": "rachel@product.mgmt", "subject": "Weather Comparison", "body_preview": "The warmer city is Warsaw at 95.0°F", "cc": null, "timestamp": "2026-02-22T12:00:00"} | 4 | 6 | dag | parallel-merge-chain |
L3_dag_0155 | L3_dag | Compare weather in Athens and Montreal, convert the warmer temp to Fahrenheit, and email the result to james@logistics.ship. | [
"get_weather",
"unit_convert",
"send_email",
"extract_links",
"add_duration",
"extract_numbers"
] | {"steps": [{"step_id": "step_1", "tool_name": "get_weather", "arguments": {"city": "Athens"}, "depends_on": [], "output_key": "w1"}, {"step_id": "step_2", "tool_name": "get_weather", "arguments": {"city": "Montreal"}, "depends_on": [], "output_key": "w2"}, {"step_id": "step_3", "tool_name": "unit_convert", "arguments": {"value": 29, "from_unit": "celsius", "to_unit": "fahrenheit"}, "depends_on": ["step_1", "step_2"], "output_key": null}, {"step_id": "step_4", "tool_name": "send_email", "arguments": {"to": "james@logistics.ship", "subject": "Weather Comparison", "body": "The warmer city is Montreal at 84.2°F"}, "depends_on": ["step_3"], "output_key": null}], "final_answer_source": "step_4"} | {"status": "sent", "message_id": "msg_9b8505bd6b1a", "to": "james@logistics.ship", "subject": "Weather Comparison", "body_preview": "The warmer city is Montreal at 84.2°F", "cc": null, "timestamp": "2026-02-22T12:00:00"} | 4 | 6 | dag | parallel-merge-chain |
L3_dag_0156 | L3_dag | Load /data/employees.csv, extract numbers from its contents, calculate stats (mean, median, etc.), and save to /data/numbers_report.json. | [
"read_file",
"extract_numbers",
"statistical_analysis",
"write_file",
"database_query",
"encrypt_text",
"get_current_time"
] | {"steps": [{"step_id": "step_1", "tool_name": "read_file", "arguments": {"path": "/data/employees.csv"}, "depends_on": [], "output_key": "file_data"}, {"step_id": "step_2", "tool_name": "extract_numbers", "arguments": {"text": "name,department,salary,years\nAlice,Engineering,95000,5\nBob,Marketing,72000,3\nCharlie,Engineering,105000,8\nDiana,Sales,68000,2\nEve,Engineering,88000,4\nFrank,Marketing,76000,6\nGrace,Sales,71000,3\nHenry,Engineering,112000,10"}, "depends_on": ["step_1"], "output_key": "extracted"}, {"step_id": "step_3", "tool_name": "statistical_analysis", "arguments": {"numbers": [950, 0, 5, 720, 0, 3, 105, 0, 8, 680, 0, 2, 880, 0, 4, 760, 0, 6, 710, 0, 3, 112, 0, 10]}, "depends_on": ["step_2"], "output_key": "stats"}, {"step_id": "step_4", "tool_name": "write_file", "arguments": {"path": "/data/numbers_report.json", "content": "{\"source_file\": \"/data/employees.csv\", \"numbers\": [950, 0, 5, 720, 0, 3, 105, 0, 8, 680, 0, 2, 880, 0, 4, 760, 0, 6, 710, 0, 3, 112, 0, 10], \"statistics\": {\"count\": 24, \"mean\": 206.5833, \"median\": 4.5, \"min\": 0.0, \"max\": 950.0, \"sum\": 4958.0, \"stdev\": 345.0893}}"}, "depends_on": ["step_3"], "output_key": null}], "final_answer_source": "step_4"} | {"status": "written", "path": "/data/numbers_report.json", "size": 262, "type": "application/json", "overwrote_existing": false} | 4 | 7 | dag | linear-chain-4-file-stats |
L3_dag_0157 | L3_dag | Open /data/report.txt, pull out numeric values, run statistical analysis, and write the output to /data/stats_output.json. | [
"read_file",
"extract_numbers",
"statistical_analysis",
"write_file",
"base64_encode",
"knowledge_base_query",
"create_invoice"
] | {"steps": [{"step_id": "step_1", "tool_name": "read_file", "arguments": {"path": "/data/report.txt"}, "depends_on": [], "output_key": "file_data"}, {"step_id": "step_2", "tool_name": "extract_numbers", "arguments": {"text": "Q1 2026 Sales Report\n\nTotal revenue: $2.4M\nGrowth: 15% YoY\nTop product: Enterprise Plan ($1.2M)\nNew customers: 340\nChurn rate: 2.1%\n\nKey highlights:\n- Enterprise segment grew 28%\n- APAC region exceeded targets by 12%\n- Customer satisfaction score: 4.6/5.0"}, "depends_on": ["step_1"], "output_key": "extracted"}, {"step_id": "step_3", "tool_name": "statistical_analysis", "arguments": {"numbers": [1, 202, 6, 2.4, 15, 1.2, 340, 2.1, 28, 12, 4.6, 5]}, "depends_on": ["step_2"], "output_key": "stats"}, {"step_id": "step_4", "tool_name": "write_file", "arguments": {"path": "/data/stats_output.json", "content": "{\"source_file\": \"/data/report.txt\", \"numbers\": [1, 202, 6, 2.4, 15, 1.2, 340, 2.1, 28, 12, 4.6, 5], \"statistics\": {\"count\": 12, \"mean\": 51.6083, \"median\": 5.5, \"min\": 1.0, \"max\": 340.0, \"sum\": 619.3, \"stdev\": 106.8945}}"}, "depends_on": ["step_3"], "output_key": null}], "final_answer_source": "step_4"} | {"status": "written", "path": "/data/stats_output.json", "size": 219, "type": "application/json", "overwrote_existing": false} | 4 | 7 | dag | linear-chain-4-file-stats |
L3_dag_0158 | L3_dag | Read the file at /data/employees.csv, extract all numbers, compute statistics, and save the results to /data/numbers_report.json. | [
"read_file",
"extract_numbers",
"statistical_analysis",
"write_file",
"generate_report",
"min_max",
"format_number"
] | {"steps": [{"step_id": "step_1", "tool_name": "read_file", "arguments": {"path": "/data/employees.csv"}, "depends_on": [], "output_key": "file_data"}, {"step_id": "step_2", "tool_name": "extract_numbers", "arguments": {"text": "name,department,salary,years\nAlice,Engineering,95000,5\nBob,Marketing,72000,3\nCharlie,Engineering,105000,8\nDiana,Sales,68000,2\nEve,Engineering,88000,4\nFrank,Marketing,76000,6\nGrace,Sales,71000,3\nHenry,Engineering,112000,10"}, "depends_on": ["step_1"], "output_key": "extracted"}, {"step_id": "step_3", "tool_name": "statistical_analysis", "arguments": {"numbers": [950, 0, 5, 720, 0, 3, 105, 0, 8, 680, 0, 2, 880, 0, 4, 760, 0, 6, 710, 0, 3, 112, 0, 10]}, "depends_on": ["step_2"], "output_key": "stats"}, {"step_id": "step_4", "tool_name": "write_file", "arguments": {"path": "/data/numbers_report.json", "content": "{\"source_file\": \"/data/employees.csv\", \"numbers\": [950, 0, 5, 720, 0, 3, 105, 0, 8, 680, 0, 2, 880, 0, 4, 760, 0, 6, 710, 0, 3, 112, 0, 10], \"statistics\": {\"count\": 24, \"mean\": 206.5833, \"median\": 4.5, \"min\": 0.0, \"max\": 950.0, \"sum\": 4958.0, \"stdev\": 345.0893}}"}, "depends_on": ["step_3"], "output_key": null}], "final_answer_source": "step_4"} | {"status": "written", "path": "/data/numbers_report.json", "size": 262, "type": "application/json", "overwrote_existing": true} | 4 | 7 | dag | linear-chain-4-file-stats |
L3_dag_0159 | L3_dag | Search for "circular economy business models", extract keywords from the results, search again using the top keyword, and summarize the final results. | [
"web_search",
"keyword_extract",
"summarize_text",
"execute_python",
"extract_numbers",
"standard_deviation"
] | {"steps": [{"step_id": "step_1", "tool_name": "web_search", "arguments": {"query": "circular economy business models"}, "depends_on": [], "output_key": "results1"}, {"step_id": "step_2", "tool_name": "keyword_extract", "arguments": {"text": "[{\"title\": \"Circular Economy Business Models \\u2014 Wikipedia Overview\", \"url\": \"https://github.com/article/46595\", \"snippet\": \"A comprehensive overview of circular economy business models covering key concepts, recent developments, and practical applications.\", \"source\": \"Wikipedia\"}, {\"title\": \"Circular Economy Business Models \\u2014 Forum Analysis\", \"url\": \"https://reuters.com/article/97239\", \"snippet\": \"Comparing different approaches to circular economy business models \\u2014 strengths, limi"}, "depends_on": ["step_1"], "output_key": "keywords"}, {"step_id": "step_3", "tool_name": "web_search", "arguments": {"query": "circular"}, "depends_on": ["step_2"], "output_key": "results2"}, {"step_id": "step_4", "tool_name": "summarize_text", "arguments": {"text": "[{\"title\": \"Circular \\u2014 News Overview\", \"url\": \"https://stackoverflow.com/article/91586\", \"snippet\": \"Latest research findings on circular from leading institutions and peer-reviewed publications.\", \"source\": \"News\"}, {\"title\": \"Circular \\u2014 Academic Analysis\", \"url\": \"https://sciencedirect.com/article/52193\", \"snippet\": \"Expert guide to circular with detailed explanations, examples, and best practices for practitioners.\", \"source\": \"Academic\"}, {\"title\": \"Circular \\u2014 News Guide\", \"ur"}, "depends_on": ["step_3"], "output_key": "summary"}], "final_answer_source": "step_4"} | {"original_length": 500, "summary": "[{\"title\": \"Circular \\u2014 News Overview\", \"url\": \"https://stackoverflow.com/article/91586\", \"snippet\": \"Latest research findings on circular from leading institutions and peer-reviewed publications.\", \"source\": \"News\"}, {\"title\": \"Circular \\u2014 Academic Analysis\", \"url\": \"https://sciencedirect.com/article/52193\", \"snippet\": \"Expert guide to circular with detailed explanations, examples, and best practices for practitioners.\", \"source\": \"Academic\"}, {\"title\": \"Circular \\u2014 News Guide\", \"ur", "summary_length": 500, "sentences_used": 1, "compression_ratio": 1.0} | 4 | 6 | dag | multi-hop-search |
L3_dag_0160 | L3_dag | Do a web search for "digital twin technology", find the most important keywords, perform a follow-up search with the top keyword, then summarize. | [
"web_search",
"keyword_extract",
"summarize_text",
"json_extract",
"generate_summary_stats",
"slugify"
] | {"steps": [{"step_id": "step_1", "tool_name": "web_search", "arguments": {"query": "digital twin technology"}, "depends_on": [], "output_key": "results1"}, {"step_id": "step_2", "tool_name": "keyword_extract", "arguments": {"text": "[{\"title\": \"Digital Twin Technology \\u2014 Wikipedia Overview\", \"url\": \"https://en.wikipedia.org/article/40290\", \"snippet\": \"A comprehensive overview of digital twin technology covering key concepts, recent developments, and practical applications.\", \"source\": \"Wikipedia\"}, {\"title\": \"Digital Twin Technology \\u2014 Academic Analysis\", \"url\": \"https://sciencedirect.com/article/35383\", \"snippet\": \"Expert guide to digital twin technology with detailed explanations, examples, and best practices for "}, "depends_on": ["step_1"], "output_key": "keywords"}, {"step_id": "step_3", "tool_name": "web_search", "arguments": {"query": "digital"}, "depends_on": ["step_2"], "output_key": "results2"}, {"step_id": "step_4", "tool_name": "summarize_text", "arguments": {"text": "[{\"title\": \"Digital \\u2014 Academic Overview\", \"url\": \"https://bbc.com/article/73418\", \"snippet\": \"Expert guide to digital with detailed explanations, examples, and best practices for practitioners.\", \"source\": \"Academic\"}, {\"title\": \"Digital \\u2014 Blog Analysis\", \"url\": \"https://nytimes.com/article/47637\", \"snippet\": \"digital: analysis of current trends, methodologies, and future directions in the field.\", \"source\": \"Blog\"}, {\"title\": \"Digital \\u2014 Forum Guide\", \"url\": \"https://medium.com/ar"}, "depends_on": ["step_3"], "output_key": "summary"}], "final_answer_source": "step_4"} | {"original_length": 500, "summary": "[{\"title\": \"Digital \\u2014 Academic Overview\", \"url\": \"https://bbc.com/article/73418\", \"snippet\": \"Expert guide to digital with detailed explanations, examples, and best practices for practitioners.\", \"source\": \"Academic\"}, {\"title\": \"Digital \\u2014 Blog Analysis\", \"url\": \"https://nytimes.com/article/47637\", \"snippet\": \"digital: analysis of current trends, methodologies, and future directions in the field.\", \"source\": \"Blog\"}, {\"title\": \"Digital \\u2014 Forum Guide\", \"url\": \"https://medium.com/ar", "summary_length": 500, "sentences_used": 1, "compression_ratio": 1.0} | 4 | 6 | dag | multi-hop-search |
L3_dag_0161 | L3_dag | Do a web search for "remote work productivity tools", find the most important keywords, perform a follow-up search with the top keyword, then summarize. | [
"web_search",
"keyword_extract",
"summarize_text",
"min_max",
"get_directions",
"spell_check"
] | {"steps": [{"step_id": "step_1", "tool_name": "web_search", "arguments": {"query": "remote work productivity tools"}, "depends_on": [], "output_key": "results1"}, {"step_id": "step_2", "tool_name": "keyword_extract", "arguments": {"text": "[{\"title\": \"Remote Work Productivity Tools \\u2014 Blog Overview\", \"url\": \"https://nytimes.com/article/33047\", \"snippet\": \"remote work productivity tools: analysis of current trends, methodologies, and future directions in the field.\", \"source\": \"Blog\"}, {\"title\": \"Remote Work Productivity Tools \\u2014 Academic Analysis\", \"url\": \"https://bbc.com/article/88688\", \"snippet\": \"Expert guide to remote work productivity tools with detailed explanations, examples, and best practices for practitioners.\", "}, "depends_on": ["step_1"], "output_key": "keywords"}, {"step_id": "step_3", "tool_name": "web_search", "arguments": {"query": "remote"}, "depends_on": ["step_2"], "output_key": "results2"}, {"step_id": "step_4", "tool_name": "summarize_text", "arguments": {"text": "[{\"title\": \"Remote \\u2014 Wikipedia Overview\", \"url\": \"https://github.com/article/24535\", \"snippet\": \"A comprehensive overview of remote covering key concepts, recent developments, and practical applications.\", \"source\": \"Wikipedia\"}, {\"title\": \"Remote \\u2014 Blog Analysis\", \"url\": \"https://nature.com/article/48532\", \"snippet\": \"remote: analysis of current trends, methodologies, and future directions in the field.\", \"source\": \"Blog\"}, {\"title\": \"Remote \\u2014 News Guide\", \"url\": \"https://stackov"}, "depends_on": ["step_3"], "output_key": "summary"}], "final_answer_source": "step_4"} | {"original_length": 500, "summary": "[{\"title\": \"Remote \\u2014 Wikipedia Overview\", \"url\": \"https://github.com/article/24535\", \"snippet\": \"A comprehensive overview of remote covering key concepts, recent developments, and practical applications.\", \"source\": \"Wikipedia\"}, {\"title\": \"Remote \\u2014 Blog Analysis\", \"url\": \"https://nature.com/article/48532\", \"snippet\": \"remote: analysis of current trends, methodologies, and future directions in the field.\", \"source\": \"Blog\"}, {\"title\": \"Remote \\u2014 News Guide\", \"url\": \"https://stackov", "summary_length": 500, "sentences_used": 1, "compression_ratio": 1.0} | 4 | 6 | dag | multi-hop-search |
L3_dag_0162 | L3_dag | Detect the language of the text "The Internet of Things connects billions of devices worldwid...", translate it to English, summarize it, and email the summary to peter@support.help. | [
"detect_language",
"translate_text",
"summarize_text",
"send_email",
"web_search",
"tokenize_text",
"compare_texts"
] | {"steps": [{"step_id": "step_1", "tool_name": "detect_language", "arguments": {"text": "The Internet of Things connects billions of devices worldwide. Smart home technology is becoming standard in new construction."}, "depends_on": [], "output_key": "detected"}, {"step_id": "step_2", "tool_name": "translate_text", "arguments": {"text": "The Internet of Things connects billions of devices worldwide. Smart home technology is becoming standard in new construction.", "from_language": "en", "to_language": "en"}, "depends_on": ["step_1"], "output_key": "translated"}, {"step_id": "step_3", "tool_name": "summarize_text", "arguments": {"text": "The Internet of Things connects billions of devices worldwide. Smart home technology is becoming standard in new construction. (English translation)"}, "depends_on": ["step_2"], "output_key": "summary"}, {"step_id": "step_4", "tool_name": "send_email", "arguments": {"to": "peter@support.help", "subject": "Translated Summary", "body": "The Internet of Things connects billions of devices worldwide. Smart home technology is becoming standard in new construction. (English translation)"}, "depends_on": ["step_3"], "output_key": null}], "final_answer_source": "step_4"} | {"status": "sent", "message_id": "msg_3ae88fff7156", "to": "peter@support.help", "subject": "Translated Summary", "body_preview": "The Internet of Things connects billions of devices worldwide. Smart home technology is becoming sta...", "cc": null, "timestamp": "2026-02-22T12:00:00"} | 4 | 7 | dag | linear-chain-4-nlp |
L3_dag_0163 | L3_dag | Figure out what language "Autonomous vehicles are getting closer to widespread deploym..." is in, translate to English, create a summary, then send it to sam@operations.ops. | [
"detect_language",
"translate_text",
"summarize_text",
"send_email",
"split_text",
"min_max",
"create_task"
] | {"steps": [{"step_id": "step_1", "tool_name": "detect_language", "arguments": {"text": "Autonomous vehicles are getting closer to widespread deployment. Safety regulations and infrastructure updates are key challenges."}, "depends_on": [], "output_key": "detected"}, {"step_id": "step_2", "tool_name": "translate_text", "arguments": {"text": "Autonomous vehicles are getting closer to widespread deployment. Safety regulations and infrastructure updates are key challenges.", "from_language": "en", "to_language": "en"}, "depends_on": ["step_1"], "output_key": "translated"}, {"step_id": "step_3", "tool_name": "summarize_text", "arguments": {"text": "Autonomous vehicles are getting closer to widespread deployment. Safety regulations and infrastructure updates are key challenges. (English translation)"}, "depends_on": ["step_2"], "output_key": "summary"}, {"step_id": "step_4", "tool_name": "send_email", "arguments": {"to": "sam@operations.ops", "subject": "Translated Summary", "body": "Autonomous vehicles are getting closer to widespread deployment. Safety regulations and infrastructure updates are key challenges. (English translation)"}, "depends_on": ["step_3"], "output_key": null}], "final_answer_source": "step_4"} | {"status": "sent", "message_id": "msg_3a91d3fa4314", "to": "sam@operations.ops", "subject": "Translated Summary", "body_preview": "Autonomous vehicles are getting closer to widespread deployment. Safety regulations and infrastructu...", "cc": null, "timestamp": "2026-02-22T12:00:00"} | 4 | 7 | dag | linear-chain-4-nlp |
L3_dag_0164 | L3_dag | Figure out what language "3D printing technology is advancing into new materials and a..." is in, translate to English, create a summary, then send it to victor@infra.cloud. | [
"detect_language",
"translate_text",
"summarize_text",
"send_email",
"percentage_change",
"create_notification",
"classify_text"
] | {"steps": [{"step_id": "step_1", "tool_name": "detect_language", "arguments": {"text": "3D printing technology is advancing into new materials and applications. From medical implants to housing construction, possibilities expand."}, "depends_on": [], "output_key": "detected"}, {"step_id": "step_2", "tool_name": "translate_text", "arguments": {"text": "3D printing technology is advancing into new materials and applications. From medical implants to housing construction, possibilities expand.", "from_language": "en", "to_language": "en"}, "depends_on": ["step_1"], "output_key": "translated"}, {"step_id": "step_3", "tool_name": "summarize_text", "arguments": {"text": "3D printing technology is advancing into new materials and applications. From medical implants to housing construction, possibilities expand. (English translation)"}, "depends_on": ["step_2"], "output_key": "summary"}, {"step_id": "step_4", "tool_name": "send_email", "arguments": {"to": "victor@infra.cloud", "subject": "Translated Summary", "body": "3D printing technology is advancing into new materials and applications. From medical implants to housing construction, possibilities expand. (English translation)"}, "depends_on": ["step_3"], "output_key": null}], "final_answer_source": "step_4"} | {"status": "sent", "message_id": "msg_4dde26d85d27", "to": "victor@infra.cloud", "subject": "Translated Summary", "body_preview": "3D printing technology is advancing into new materials and applications. From medical implants to ho...", "cc": null, "timestamp": "2026-02-22T12:00:00"} | 4 | 7 | dag | linear-chain-4-nlp |
L3_dag_0165 | L3_dag | Look up BAC and ZM stocks, find the percentage difference, and notify about the comparison. | [
"get_stock_price",
"percentage_change",
"create_notification",
"statistical_analysis",
"calculate_date_diff",
"extract_links"
] | {"steps": [{"step_id": "step_1", "tool_name": "get_stock_price", "arguments": {"symbol": "BAC"}, "depends_on": [], "output_key": "s1"}, {"step_id": "step_2", "tool_name": "get_stock_price", "arguments": {"symbol": "ZM"}, "depends_on": [], "output_key": "s2"}, {"step_id": "step_3", "tool_name": "percentage_change", "arguments": {"old_value": 209, "new_value": 226}, "depends_on": ["step_1", "step_2"], "output_key": null}, {"step_id": "step_4", "tool_name": "create_notification", "arguments": {"title": "Stock Comparison", "message": "ZM is 8.1% increase compared to BAC", "priority": "medium"}, "depends_on": ["step_3"], "output_key": null}], "final_answer_source": "step_4"} | {"status": "created", "notification_id": "notif_787053e1eddf", "title": "Stock Comparison", "priority": "medium", "timestamp": "2026-02-22T12:00:00"} | 4 | 6 | dag | parallel-compute-notify |
L3_dag_0166 | L3_dag | Get the prices of SHOP and JPM, compute the percent change between them, and create a notification with the result. | [
"get_stock_price",
"percentage_change",
"create_notification",
"rss_feed_parse",
"generate_url",
"merge_data"
] | {"steps": [{"step_id": "step_1", "tool_name": "get_stock_price", "arguments": {"symbol": "SHOP"}, "depends_on": [], "output_key": "s1"}, {"step_id": "step_2", "tool_name": "get_stock_price", "arguments": {"symbol": "JPM"}, "depends_on": [], "output_key": "s2"}, {"step_id": "step_3", "tool_name": "percentage_change", "arguments": {"old_value": 71, "new_value": 79}, "depends_on": ["step_1", "step_2"], "output_key": null}, {"step_id": "step_4", "tool_name": "create_notification", "arguments": {"title": "Stock Comparison", "message": "JPM is 11.3% increase compared to SHOP", "priority": "medium"}, "depends_on": ["step_3"], "output_key": null}], "final_answer_source": "step_4"} | {"status": "created", "notification_id": "notif_787053e1eddf", "title": "Stock Comparison", "priority": "medium", "timestamp": "2026-02-22T12:00:00"} | 4 | 6 | dag | parallel-compute-notify |
L3_dag_0167 | L3_dag | Get the prices of PINS and MA, compute the percent change between them, and create a notification with the result. | [
"get_stock_price",
"percentage_change",
"create_notification",
"transform_format",
"json_extract",
"search_products"
] | {"steps": [{"step_id": "step_1", "tool_name": "get_stock_price", "arguments": {"symbol": "PINS"}, "depends_on": [], "output_key": "s1"}, {"step_id": "step_2", "tool_name": "get_stock_price", "arguments": {"symbol": "MA"}, "depends_on": [], "output_key": "s2"}, {"step_id": "step_3", "tool_name": "percentage_change", "arguments": {"old_value": 236, "new_value": 148}, "depends_on": ["step_1", "step_2"], "output_key": null}, {"step_id": "step_4", "tool_name": "create_notification", "arguments": {"title": "Stock Comparison", "message": "MA is -37.3% decrease compared to PINS", "priority": "medium"}, "depends_on": ["step_3"], "output_key": null}], "final_answer_source": "step_4"} | {"status": "created", "notification_id": "notif_787053e1eddf", "title": "Stock Comparison", "priority": "medium", "timestamp": "2026-02-22T12:00:00"} | 4 | 6 | dag | parallel-compute-notify |
L3_dag_0168 | L3_dag | Look up Barcelona's weather, create a Product Roadmap event on 2026-09-12 08:30, and set a reminder about the event. | [
"get_weather",
"create_calendar_event",
"set_reminder",
"word_count",
"rss_feed_parse",
"hash_text",
"slugify"
] | {"steps": [{"step_id": "step_1", "tool_name": "get_weather", "arguments": {"city": "Barcelona"}, "depends_on": [], "output_key": "weather"}, {"step_id": "step_2", "tool_name": "create_calendar_event", "arguments": {"title": "Product Roadmap (Outdoor - cloudy)", "date": "2026-09-12 08:30", "duration_minutes": 60}, "depends_on": ["step_1"], "output_key": "event"}, {"step_id": "step_3", "tool_name": "set_reminder", "arguments": {"message": "Reminder: Product Roadmap in Barcelona. Weather in Barcelona: 15°C, cloudy", "remind_at": "2026-09-12 08:30"}, "depends_on": ["step_2"], "output_key": "reminder"}, {"step_id": "step_4", "tool_name": "word_count", "arguments": {"text": "Weather in Barcelona: 15°C, cloudy"}, "depends_on": ["step_1"], "output_key": null}], "final_answer_source": "step_3"} | {"reminder_id": "rem_280fc563ec56", "message": "Reminder: Product Roadmap in Barcelona. Weather in Barcelona: 15°C, cloudy", "remind_at": "2026-09-12 08:30", "confirmation": "Reminder set for 2026-09-12 08:30: 'Reminder: Product Roadmap in Barcelona. Weather in Barcelona: 15°C, cloudy'."} | 4 | 7 | dag | chain-with-branch |
L3_dag_0169 | L3_dag | Look up Toronto's weather, create a Data Pipeline Review event on 2026-04-15 10:00, and set a reminder about the event. | [
"get_weather",
"create_calendar_event",
"set_reminder",
"word_count",
"base64_encode",
"calculator",
"moving_average"
] | {"steps": [{"step_id": "step_1", "tool_name": "get_weather", "arguments": {"city": "Toronto"}, "depends_on": [], "output_key": "weather"}, {"step_id": "step_2", "tool_name": "create_calendar_event", "arguments": {"title": "Data Pipeline Review (Outdoor - cloudy)", "date": "2026-04-15 10:00", "duration_minutes": 60}, "depends_on": ["step_1"], "output_key": "event"}, {"step_id": "step_3", "tool_name": "set_reminder", "arguments": {"message": "Reminder: Data Pipeline Review in Toronto. Weather in Toronto: 4°C, cloudy", "remind_at": "2026-04-15 10:00"}, "depends_on": ["step_2"], "output_key": "reminder"}, {"step_id": "step_4", "tool_name": "word_count", "arguments": {"text": "Weather in Toronto: 4°C, cloudy"}, "depends_on": ["step_1"], "output_key": null}], "final_answer_source": "step_3"} | {"reminder_id": "rem_72d622d0e9bd", "message": "Reminder: Data Pipeline Review in Toronto. Weather in Toronto: 4°C, cloudy", "remind_at": "2026-04-15 10:00", "confirmation": "Reminder set for 2026-04-15 10:00: 'Reminder: Data Pipeline Review in Toronto. Weather in Toronto: 4°C, cloudy'."} | 4 | 7 | dag | chain-with-branch |
L3_dag_0170 | L3_dag | Get the weather forecast for Oslo, schedule an outdoor Budget Review for 2026-08-05 16:00, and set a reminder for it. | [
"get_weather",
"create_calendar_event",
"set_reminder",
"word_count",
"parse_html",
"parse_date",
"data_filter"
] | {"steps": [{"step_id": "step_1", "tool_name": "get_weather", "arguments": {"city": "Oslo"}, "depends_on": [], "output_key": "weather"}, {"step_id": "step_2", "tool_name": "create_calendar_event", "arguments": {"title": "Budget Review (Outdoor - rainy)", "date": "2026-08-05 16:00", "duration_minutes": 60}, "depends_on": ["step_1"], "output_key": "event"}, {"step_id": "step_3", "tool_name": "set_reminder", "arguments": {"message": "Reminder: Budget Review in Oslo. Weather in Oslo: 32°C, rainy", "remind_at": "2026-08-05 16:00"}, "depends_on": ["step_2"], "output_key": "reminder"}, {"step_id": "step_4", "tool_name": "word_count", "arguments": {"text": "Weather in Oslo: 32°C, rainy"}, "depends_on": ["step_1"], "output_key": null}], "final_answer_source": "step_3"} | {"reminder_id": "rem_c33e1cf8494c", "message": "Reminder: Budget Review in Oslo. Weather in Oslo: 32°C, rainy", "remind_at": "2026-08-05 16:00", "confirmation": "Reminder set for 2026-08-05 16:00: 'Reminder: Budget Review in Oslo. Weather in Oslo: 32°C, rainy'."} | 4 | 7 | dag | chain-with-branch |
L3_dag_0171 | L3_dag | Pull entities from two texts simultaneously, mask PII in the merged entity list, and save to /data/pii_cleaned.txt. | [
"extract_entities",
"mask_pii",
"write_file",
"text_to_number",
"get_stock_price",
"get_directions"
] | {"steps": [{"step_id": "step_1", "tool_name": "extract_entities", "arguments": {"text": "Alan Turing published his seminal paper on computability in 1936 at Cambridge."}, "depends_on": [], "output_key": "ent1"}, {"step_id": "step_2", "tool_name": "extract_entities", "arguments": {"text": "Isaac Newton published Principia Mathematica in 1687 while at Cambridge University."}, "depends_on": [], "output_key": "ent2"}, {"step_id": "step_3", "tool_name": "mask_pii", "arguments": {"text": "Alan Turing published his seminal paper on computability in 1936 at Cambridge.\n---\nIsaac Newton published Principia Mathematica in 1687 while at Cambridge University."}, "depends_on": ["step_1", "step_2"], "output_key": null}, {"step_id": "step_4", "tool_name": "write_file", "arguments": {"path": "/data/pii_cleaned.txt", "content": "{\"entities_text1\": [{\"text\": \"Alan Turing\", \"type\": \"capitalized_phrase\"}], \"entities_text2\": [{\"text\": \"Isaac Newton\", \"type\": \"capitalized_phrase\"}, {\"text\": \"Principia Mathematica\", \"type\": \"capitalized_phrase\"}, {\"text\": \"Cambridge University\", \"type\": \"capitalized_phrase\"}], \"masked_text\": \"Alan Turing published his seminal paper on computability in 1936 at Cambridge.\\n---\\nIsaac Newton published Principia Mathematica in 1687 while at Cambridge University.\"}"}, "depends_on": ["step_3"], "output_key": null}], "final_answer_source": "step_4"} | {"status": "written", "path": "/data/pii_cleaned.txt", "size": 467, "type": "text/plain", "overwrote_existing": false} | 4 | 6 | dag | parallel-process-save |
L3_dag_0172 | L3_dag | Find named entities in two different texts, then mask personally identifiable information from the combined text and write it to /data/safe_entities.txt. | [
"extract_entities",
"mask_pii",
"write_file",
"store_memory",
"create_spreadsheet",
"knowledge_base_query"
] | {"steps": [{"step_id": "step_1", "tool_name": "extract_entities", "arguments": {"text": "The conference keynote by Prof. James Chen is on January 20, 2026 at MIT."}, "depends_on": [], "output_key": "ent1"}, {"step_id": "step_2", "tool_name": "extract_entities", "arguments": {"text": "Galileo Galilei made his first telescope observations in 1609 in Padua, Italy."}, "depends_on": [], "output_key": "ent2"}, {"step_id": "step_3", "tool_name": "mask_pii", "arguments": {"text": "The conference keynote by Prof. James Chen is on January 20, 2026 at MIT.\n---\nGalileo Galilei made his first telescope observations in 1609 in Padua, Italy."}, "depends_on": ["step_1", "step_2"], "output_key": null}, {"step_id": "step_4", "tool_name": "write_file", "arguments": {"path": "/data/safe_entities.txt", "content": "{\"entities_text1\": [{\"text\": \"January 20, 2026\", \"type\": \"date\"}, {\"text\": \"20\", \"type\": \"number\"}, {\"text\": \"James Chen\", \"type\": \"capitalized_phrase\"}], \"entities_text2\": [{\"text\": \"Galileo Galilei\", \"type\": \"capitalized_phrase\"}], \"masked_text\": \"The conference keynote by Prof. James Chen is on January 20, 2026 at MIT.\\n---\\nGalileo Galilei made his first telescope observations in 1609 in Padua, Italy.\"}"}, "depends_on": ["step_3"], "output_key": null}], "final_answer_source": "step_4"} | {"status": "written", "path": "/data/safe_entities.txt", "size": 410, "type": "text/plain", "overwrote_existing": false} | 4 | 6 | dag | parallel-process-save |
L3_dag_0173 | L3_dag | Find named entities in two different texts, then mask personally identifiable information from the combined text and write it to /data/pii_cleaned.txt. | [
"extract_entities",
"mask_pii",
"write_file",
"round_number",
"extract_links",
"percentile"
] | {"steps": [{"step_id": "step_1", "tool_name": "extract_entities", "arguments": {"text": "Final exam schedule: email registrar@university.edu or visit room 301 in Admin Building."}, "depends_on": [], "output_key": "ent1"}, {"step_id": "step_2", "tool_name": "extract_entities", "arguments": {"text": "Reach out to support@techcorp.io for any issues. Our office is in San Francisco, CA."}, "depends_on": [], "output_key": "ent2"}, {"step_id": "step_3", "tool_name": "mask_pii", "arguments": {"text": "Final exam schedule: email registrar@university.edu or visit room 301 in Admin Building.\n---\nReach out to support@techcorp.io for any issues. Our office is in San Francisco, CA."}, "depends_on": ["step_1", "step_2"], "output_key": null}, {"step_id": "step_4", "tool_name": "write_file", "arguments": {"path": "/data/pii_cleaned.txt", "content": "{\"entities_text1\": [{\"text\": \"registrar@university.edu\", \"type\": \"email\"}, {\"text\": \"301\", \"type\": \"number\"}, {\"text\": \"Admin Building\", \"type\": \"capitalized_phrase\"}], \"entities_text2\": [{\"text\": \"support@techcorp.io\", \"type\": \"email\"}, {\"text\": \"San Francisco\", \"type\": \"capitalized_phrase\"}], \"masked_text\": \"Final exam schedule: email [EMAIL] or visit room 301 in Admin Building.\\n---\\nReach out to [EMAIL] for any issues. Our office is in San Francisco, CA.\"}"}, "depends_on": ["step_3"], "output_key": null}], "final_answer_source": "step_4"} | {"status": "written", "path": "/data/pii_cleaned.txt", "size": 464, "type": "text/plain", "overwrote_existing": true} | 4 | 6 | dag | parallel-process-save |
L3_dag_0174 | L3_dag | Read /data/report.txt and /data/config.json in parallel, count the words in each, calculate the percentage difference in word counts, and log the result. | [
"read_file",
"word_count",
"percentage_change",
"log_event",
"next_occurrence",
"dns_lookup",
"list_files"
] | {"steps": [{"step_id": "step_1", "tool_name": "read_file", "arguments": {"path": "/data/report.txt"}, "depends_on": [], "output_key": "f1"}, {"step_id": "step_2", "tool_name": "read_file", "arguments": {"path": "/data/config.json"}, "depends_on": [], "output_key": "f2"}, {"step_id": "step_3", "tool_name": "word_count", "arguments": {"text": "Q1 2026 Sales Report\n\nTotal revenue: $2.4M\nGrowth: 15% YoY\nTop product: Enterprise Plan ($1.2M)\nNew customers: 340\nChurn rate: 2.1%\n\nKey highlights:\n- Enterprise segment grew 28%\n- APAC region exceeded targets by 12%\n- Customer satisfaction score: 4.6/5.0"}, "depends_on": ["step_1"], "output_key": "wc1"}, {"step_id": "step_4", "tool_name": "word_count", "arguments": {"text": "{\"app_name\": \"DataPipeline\", \"version\": \"2.1.0\", \"max_workers\": 4, \"timeout_seconds\": 30, \"features\": {\"caching\": true, \"logging\": true, \"metrics\": false}}"}, "depends_on": ["step_2"], "output_key": "wc2"}, {"step_id": "step_5", "tool_name": "percentage_change", "arguments": {"old_value": 40, "new_value": 15}, "depends_on": ["step_3", "step_4"], "output_key": null}, {"step_id": "step_6", "tool_name": "log_event", "arguments": {"event_type": "word_count_comparison", "message": "Word count comparison: /data/report.txt has 40 words, /data/config.json has 15 words. Change: -62.5%", "severity": "info"}, "depends_on": ["step_5"], "output_key": null}], "final_answer_source": "step_6"} | {"log_id": "log_ff86bfc6ed08", "event_type": "word_count_comparison", "message": "Word count comparison: /data/report.txt has 40 words, /data/config.json has 15 words. Change: -62.5%", "severity": "info", "timestamp": "2026-02-22T12:00:00"} | 6 | 7 | dag | true-dag-parallel-reads-merge |
L3_dag_0175 | L3_dag | Read /data/config.json and /data/employees.csv in parallel, count the words in each, calculate the percentage difference in word counts, and log the result. | [
"read_file",
"word_count",
"percentage_change",
"log_event",
"generate_url",
"text_similarity",
"send_message"
] | {"steps": [{"step_id": "step_1", "tool_name": "read_file", "arguments": {"path": "/data/config.json"}, "depends_on": [], "output_key": "f1"}, {"step_id": "step_2", "tool_name": "read_file", "arguments": {"path": "/data/employees.csv"}, "depends_on": [], "output_key": "f2"}, {"step_id": "step_3", "tool_name": "word_count", "arguments": {"text": "{\"app_name\": \"DataPipeline\", \"version\": \"2.1.0\", \"max_workers\": 4, \"timeout_seconds\": 30, \"features\": {\"caching\": true, \"logging\": true, \"metrics\": false}}"}, "depends_on": ["step_1"], "output_key": "wc1"}, {"step_id": "step_4", "tool_name": "word_count", "arguments": {"text": "name,department,salary,years\nAlice,Engineering,95000,5\nBob,Marketing,72000,3\nCharlie,Engineering,105000,8\nDiana,Sales,68000,2\nEve,Engineering,88000,4\nFrank,Marketing,76000,6\nGrace,Sales,71000,3\nHenry,Engineering,112000,10"}, "depends_on": ["step_2"], "output_key": "wc2"}, {"step_id": "step_5", "tool_name": "percentage_change", "arguments": {"old_value": 15, "new_value": 9}, "depends_on": ["step_3", "step_4"], "output_key": null}, {"step_id": "step_6", "tool_name": "log_event", "arguments": {"event_type": "word_count_comparison", "message": "Word count comparison: /data/config.json has 15 words, /data/employees.csv has 9 words. Change: -40.0%", "severity": "info"}, "depends_on": ["step_5"], "output_key": null}], "final_answer_source": "step_6"} | {"log_id": "log_37ee7fc91fca", "event_type": "word_count_comparison", "message": "Word count comparison: /data/config.json has 15 words, /data/employees.csv has 9 words. Change: -40.0%", "severity": "info", "timestamp": "2026-02-22T12:00:00"} | 6 | 7 | dag | true-dag-parallel-reads-merge |
L3_dag_0176 | L3_dag | Open files /data/employees.csv and /data/report.txt simultaneously, get word counts for both, compute the percent change, and log it. | [
"read_file",
"word_count",
"percentage_change",
"log_event",
"detect_language",
"format_number",
"check_url_status"
] | {"steps": [{"step_id": "step_1", "tool_name": "read_file", "arguments": {"path": "/data/employees.csv"}, "depends_on": [], "output_key": "f1"}, {"step_id": "step_2", "tool_name": "read_file", "arguments": {"path": "/data/report.txt"}, "depends_on": [], "output_key": "f2"}, {"step_id": "step_3", "tool_name": "word_count", "arguments": {"text": "name,department,salary,years\nAlice,Engineering,95000,5\nBob,Marketing,72000,3\nCharlie,Engineering,105000,8\nDiana,Sales,68000,2\nEve,Engineering,88000,4\nFrank,Marketing,76000,6\nGrace,Sales,71000,3\nHenry,Engineering,112000,10"}, "depends_on": ["step_1"], "output_key": "wc1"}, {"step_id": "step_4", "tool_name": "word_count", "arguments": {"text": "Q1 2026 Sales Report\n\nTotal revenue: $2.4M\nGrowth: 15% YoY\nTop product: Enterprise Plan ($1.2M)\nNew customers: 340\nChurn rate: 2.1%\n\nKey highlights:\n- Enterprise segment grew 28%\n- APAC region exceeded targets by 12%\n- Customer satisfaction score: 4.6/5.0"}, "depends_on": ["step_2"], "output_key": "wc2"}, {"step_id": "step_5", "tool_name": "percentage_change", "arguments": {"old_value": 9, "new_value": 40}, "depends_on": ["step_3", "step_4"], "output_key": null}, {"step_id": "step_6", "tool_name": "log_event", "arguments": {"event_type": "word_count_comparison", "message": "Word count comparison: /data/employees.csv has 9 words, /data/report.txt has 40 words. Change: 344.4%", "severity": "info"}, "depends_on": ["step_5"], "output_key": null}], "final_answer_source": "step_6"} | {"log_id": "log_9d0c972b6e76", "event_type": "word_count_comparison", "message": "Word count comparison: /data/employees.csv has 9 words, /data/report.txt has 40 words. Change: 344.4%", "severity": "info", "timestamp": "2026-02-22T12:00:00"} | 6 | 7 | dag | true-dag-parallel-reads-merge |
L3_dag_0177 | L3_dag | Web search "food technology innovations", run entity extraction and sentiment analysis on the results at the same time, create a report, and email iris@media.news. | [
"web_search",
"extract_entities",
"sentiment_analysis",
"generate_report",
"send_email",
"spell_check",
"base64_decode",
"detect_language"
] | {"steps": [{"step_id": "step_1", "tool_name": "web_search", "arguments": {"query": "food technology innovations"}, "depends_on": [], "output_key": "results"}, {"step_id": "step_2", "tool_name": "extract_entities", "arguments": {"text": "[{\"title\": \"Food Technology Innovations \\u2014 Forum Overview\", \"url\": \"https://medium.com/article/24314\", \"snippet\": \"Comparing different approaches to food technology innovations \\u2014 strengths, limitations, and real-world performance.\", \"source\": \"Forum\"}, {\"title\": \"Food Technology Innovations \\u2014 Wikipedia Analysis\", \"url\": \"https://en.wikipedia.org/article/89060\", \"snippet\": \"A comprehensive overview of food technology innovations covering key concepts, recent developments, and practi"}, "depends_on": ["step_1"], "output_key": "entities"}, {"step_id": "step_3", "tool_name": "sentiment_analysis", "arguments": {"text": "[{\"title\": \"Food Technology Innovations \\u2014 Forum Overview\", \"url\": \"https://medium.com/article/24314\", \"snippet\": \"Comparing different approaches to food technology innovations \\u2014 strengths, limitations, and real-world performance.\", \"source\": \"Forum\"}, {\"title\": \"Food Technology Innovations \\u2014 Wikipedia Analysis\", \"url\": \"https://en.wikipedia.org/article/89060\", \"snippet\": \"A comprehensive overview of food technology innovations covering key concepts, recent developments, and practi"}, "depends_on": ["step_1"], "output_key": "sentiment"}, {"step_id": "step_4", "tool_name": "generate_report", "arguments": {"title": "Analysis Report: food technology innovations", "data": "{\"query\": \"food technology innovations\", \"entities_found\": 5, \"top_entities\": [\"https://medium.com/article/24314\", \"https://en.wikipedia.org/article/89060\", \"Food Technology Innovations\", \"Forum Overview\", \"Wikipedia Analysis\"], \"sentiment\": \"neutral\", \"sentiment_confidence\": 0.5}", "format": "text"}, "depends_on": ["step_2", "step_3"], "output_key": "report"}, {"step_id": "step_5", "tool_name": "send_email", "arguments": {"to": "iris@media.news", "subject": "Analysis: food technology innovations", "body": "ANALYSIS REPORT: FOOD TECHNOLOGY INNOVATIONS\n============================================\n\nSummary:\n query: food technology innovations\n entities_found: 5\n top_entities: ['https://medium.com/article/24314', 'https://en.wikipedia.org/article/89060', 'Food Technology Innovations', 'Forum Overview', 'Wikipedia Analysis']\n sentiment: neutral\n sentiment_confidence: 0.5\n\nGenerated: 2026-02-22T12:00:00"}, "depends_on": ["step_4"], "output_key": null}], "final_answer_source": "step_5"} | {"status": "sent", "message_id": "msg_20b13cfb9b25", "to": "iris@media.news", "subject": "Analysis: food technology innovations", "body_preview": "ANALYSIS REPORT: FOOD TECHNOLOGY INNOVATIONS\n============================================\n\nSummary:\n...", "cc": null, "timestamp": "2026-02-22T12:00:00"} | 5 | 8 | dag | chain-fanout-merge-chain |
L3_dag_0178 | L3_dag | Search for "artificial intelligence in healthcare", extract entities and analyze sentiment in parallel, generate a report, and email it to victor@infra.cloud. | [
"web_search",
"extract_entities",
"sentiment_analysis",
"generate_report",
"send_email",
"create_invoice",
"schedule_meeting",
"json_extract"
] | {"steps": [{"step_id": "step_1", "tool_name": "web_search", "arguments": {"query": "artificial intelligence in healthcare"}, "depends_on": [], "output_key": "results"}, {"step_id": "step_2", "tool_name": "extract_entities", "arguments": {"text": "[{\"title\": \"Artificial Intelligence In Healthcare \\u2014 Academic Overview\", \"url\": \"https://bbc.com/article/30658\", \"snippet\": \"Expert guide to artificial intelligence in healthcare with detailed explanations, examples, and best practices for practitioners.\", \"source\": \"Academic\"}, {\"title\": \"Artificial Intelligence In Healthcare \\u2014 Forum Analysis\", \"url\": \"https://reuters.com/article/93209\", \"snippet\": \"Comparing different approaches to artificial intelligence in healthcare \\u2014 strength"}, "depends_on": ["step_1"], "output_key": "entities"}, {"step_id": "step_3", "tool_name": "sentiment_analysis", "arguments": {"text": "[{\"title\": \"Artificial Intelligence In Healthcare \\u2014 Academic Overview\", \"url\": \"https://bbc.com/article/30658\", \"snippet\": \"Expert guide to artificial intelligence in healthcare with detailed explanations, examples, and best practices for practitioners.\", \"source\": \"Academic\"}, {\"title\": \"Artificial Intelligence In Healthcare \\u2014 Forum Analysis\", \"url\": \"https://reuters.com/article/93209\", \"snippet\": \"Comparing different approaches to artificial intelligence in healthcare \\u2014 strength"}, "depends_on": ["step_1"], "output_key": "sentiment"}, {"step_id": "step_4", "tool_name": "generate_report", "arguments": {"title": "Analysis Report: artificial intelligence in healthcare", "data": "{\"query\": \"artificial intelligence in healthcare\", \"entities_found\": 5, \"top_entities\": [\"https://bbc.com/article/30658\", \"https://reuters.com/article/93209\", \"Artificial Intelligence In Healthcare\", \"Academic Overview\", \"Forum Analysis\"], \"sentiment\": \"positive\", \"sentiment_confidence\": 1.0}", "format": "text"}, "depends_on": ["step_2", "step_3"], "output_key": "report"}, {"step_id": "step_5", "tool_name": "send_email", "arguments": {"to": "victor@infra.cloud", "subject": "Analysis: artificial intelligence in healthcare", "body": "ANALYSIS REPORT: ARTIFICIAL INTELLIGENCE IN HEALTHCARE\n======================================================\n\nSummary:\n query: artificial intelligence in healthcare\n entities_found: 5\n top_entities: ['https://bbc.com/article/30658', 'https://reuters.com/article/93209', 'Artificial Intelligence In Healthcare', 'Academic Overview', 'Forum Analysis']\n sentiment: positive\n sentiment_confidence: 1.0\n\nGenerated: 2026-02-22T12:00:00"}, "depends_on": ["step_4"], "output_key": null}], "final_answer_source": "step_5"} | {"status": "sent", "message_id": "msg_cb7a1a931cd7", "to": "victor@infra.cloud", "subject": "Analysis: artificial intelligence in healthcare", "body_preview": "ANALYSIS REPORT: ARTIFICIAL INTELLIGENCE IN HEALTHCARE\n=============================================...", "cc": null, "timestamp": "2026-02-22T12:00:00"} | 5 | 8 | dag | chain-fanout-merge-chain |
L3_dag_0179 | L3_dag | Look up "neurotechnology developments", then simultaneously extract entities and determine sentiment, compile a report, and send it to rachel@product.mgmt. | [
"web_search",
"extract_entities",
"sentiment_analysis",
"generate_report",
"send_email",
"url_parse",
"list_files",
"hash_text"
] | {"steps": [{"step_id": "step_1", "tool_name": "web_search", "arguments": {"query": "neurotechnology developments"}, "depends_on": [], "output_key": "results"}, {"step_id": "step_2", "tool_name": "extract_entities", "arguments": {"text": "[{\"title\": \"Neurotechnology Developments \\u2014 News Overview\", \"url\": \"https://arxiv.org/article/37971\", \"snippet\": \"Latest research findings on neurotechnology developments from leading institutions and peer-reviewed publications.\", \"source\": \"News\"}, {\"title\": \"Neurotechnology Developments \\u2014 Wikipedia Analysis\", \"url\": \"https://github.com/article/69265\", \"snippet\": \"A comprehensive overview of neurotechnology developments covering key concepts, recent developments, and practical applicat"}, "depends_on": ["step_1"], "output_key": "entities"}, {"step_id": "step_3", "tool_name": "sentiment_analysis", "arguments": {"text": "[{\"title\": \"Neurotechnology Developments \\u2014 News Overview\", \"url\": \"https://arxiv.org/article/37971\", \"snippet\": \"Latest research findings on neurotechnology developments from leading institutions and peer-reviewed publications.\", \"source\": \"News\"}, {\"title\": \"Neurotechnology Developments \\u2014 Wikipedia Analysis\", \"url\": \"https://github.com/article/69265\", \"snippet\": \"A comprehensive overview of neurotechnology developments covering key concepts, recent developments, and practical applicat"}, "depends_on": ["step_1"], "output_key": "sentiment"}, {"step_id": "step_4", "tool_name": "generate_report", "arguments": {"title": "Analysis Report: neurotechnology developments", "data": "{\"query\": \"neurotechnology developments\", \"entities_found\": 5, \"top_entities\": [\"https://arxiv.org/article/37971\", \"https://github.com/article/69265\", \"Neurotechnology Developments\", \"News Overview\", \"Wikipedia Analysis\"], \"sentiment\": \"neutral\", \"sentiment_confidence\": 0.5}", "format": "text"}, "depends_on": ["step_2", "step_3"], "output_key": "report"}, {"step_id": "step_5", "tool_name": "send_email", "arguments": {"to": "rachel@product.mgmt", "subject": "Analysis: neurotechnology developments", "body": "ANALYSIS REPORT: NEUROTECHNOLOGY DEVELOPMENTS\n=============================================\n\nSummary:\n query: neurotechnology developments\n entities_found: 5\n top_entities: ['https://arxiv.org/article/37971', 'https://github.com/article/69265', 'Neurotechnology Developments', 'News Overview', 'Wikipedia Analysis']\n sentiment: neutral\n sentiment_confidence: 0.5\n\nGenerated: 2026-02-22T12:00:00"}, "depends_on": ["step_4"], "output_key": null}], "final_answer_source": "step_5"} | {"status": "sent", "message_id": "msg_1c044bdd7549", "to": "rachel@product.mgmt", "subject": "Analysis: neurotechnology developments", "body_preview": "ANALYSIS REPORT: NEUROTECHNOLOGY DEVELOPMENTS\n=============================================\n\nSummary...", "cc": null, "timestamp": "2026-02-22T12:00:00"} | 5 | 8 | dag | chain-fanout-merge-chain |
L3_dag_0180 | L3_dag | Exchange 100 USD into TRY, SEK, and AED in parallel, determine which gives the highest and lowest amounts, and format the output. | [
"get_exchange_rate",
"min_max",
"format_number",
"base64_encode",
"text_to_number",
"is_business_day"
] | {"steps": [{"step_id": "step_1", "tool_name": "get_exchange_rate", "arguments": {"from_currency": "USD", "to_currency": "TRY", "amount": 100}, "depends_on": [], "output_key": "conv1"}, {"step_id": "step_2", "tool_name": "get_exchange_rate", "arguments": {"from_currency": "USD", "to_currency": "SEK", "amount": 100}, "depends_on": [], "output_key": "conv2"}, {"step_id": "step_3", "tool_name": "get_exchange_rate", "arguments": {"from_currency": "USD", "to_currency": "AED", "amount": 100}, "depends_on": [], "output_key": "conv3"}, {"step_id": "step_4", "tool_name": "min_max", "arguments": {"numbers": [100.0, 1042.0, 100.0]}, "depends_on": ["step_1", "step_2", "step_3"], "output_key": null}, {"step_id": "step_5", "tool_name": "format_number", "arguments": {"value": 1042.0, "format": "comma"}, "depends_on": ["step_4"], "output_key": null}], "final_answer_source": "step_5"} | {"formatted": "1,042.00"} | 5 | 6 | dag | fan-out-aggregate-format |
L3_dag_0181 | L3_dag | Convert 250 USD to GBP, BRL, and NOK simultaneously, find the min and max converted values, and format the result. | [
"get_exchange_rate",
"min_max",
"format_number",
"format_date",
"merge_data",
"retrieve_memory"
] | {"steps": [{"step_id": "step_1", "tool_name": "get_exchange_rate", "arguments": {"from_currency": "USD", "to_currency": "GBP", "amount": 250}, "depends_on": [], "output_key": "conv1"}, {"step_id": "step_2", "tool_name": "get_exchange_rate", "arguments": {"from_currency": "USD", "to_currency": "BRL", "amount": 250}, "depends_on": [], "output_key": "conv2"}, {"step_id": "step_3", "tool_name": "get_exchange_rate", "arguments": {"from_currency": "USD", "to_currency": "NOK", "amount": 250}, "depends_on": [], "output_key": "conv3"}, {"step_id": "step_4", "tool_name": "min_max", "arguments": {"numbers": [197.5, 1242.5, 250.0]}, "depends_on": ["step_1", "step_2", "step_3"], "output_key": null}, {"step_id": "step_5", "tool_name": "format_number", "arguments": {"value": 1242.5, "format": "comma"}, "depends_on": ["step_4"], "output_key": null}], "final_answer_source": "step_5"} | {"formatted": "1,242.50"} | 5 | 6 | dag | fan-out-aggregate-format |
L3_dag_0182 | L3_dag | Convert 250 USD to three currencies (HKD, SEK, SGD), find the best and worst rates, and format the numbers. | [
"get_exchange_rate",
"min_max",
"format_number",
"extract_links",
"truncate_text",
"list_memories"
] | {"steps": [{"step_id": "step_1", "tool_name": "get_exchange_rate", "arguments": {"from_currency": "USD", "to_currency": "HKD", "amount": 250}, "depends_on": [], "output_key": "conv1"}, {"step_id": "step_2", "tool_name": "get_exchange_rate", "arguments": {"from_currency": "USD", "to_currency": "SEK", "amount": 250}, "depends_on": [], "output_key": "conv2"}, {"step_id": "step_3", "tool_name": "get_exchange_rate", "arguments": {"from_currency": "USD", "to_currency": "SGD", "amount": 250}, "depends_on": [], "output_key": "conv3"}, {"step_id": "step_4", "tool_name": "min_max", "arguments": {"numbers": [1955.0, 2605.0, 335.0]}, "depends_on": ["step_1", "step_2", "step_3"], "output_key": null}, {"step_id": "step_5", "tool_name": "format_number", "arguments": {"value": 2605.0, "format": "comma"}, "depends_on": ["step_4"], "output_key": null}], "final_answer_source": "step_5"} | {"formatted": "2,605.00"} | 5 | 6 | dag | fan-out-aggregate-format |
L3_dag_0183 | L3_dag | Answer "What is the tallest mountain?" from the knowledge base, translate to Greek, then both hash the translated text and email xander@devops.ci at the same time. | [
"knowledge_base_query",
"translate_text",
"hash_text",
"send_email",
"get_weather",
"generate_summary_stats",
"base64_decode"
] | {"steps": [{"step_id": "step_1", "tool_name": "knowledge_base_query", "arguments": {"query": "What is the tallest mountain?"}, "depends_on": [], "output_key": "kb_result"}, {"step_id": "step_2", "tool_name": "translate_text", "arguments": {"text": "Based on available knowledge, What is the tallest mountain? relates to a topic with multiple facets. Further research may be needed for a complete answer.", "from_language": "en", "to_language": "el"}, "depends_on": ["step_1"], "output_key": "translated"}, {"step_id": "step_3", "tool_name": "hash_text", "arguments": {"text": "Based on available knowledge, What is the tallest mountain? relates to a topic with multiple facets. Further research may be needed for a complete answer. (EL translation)", "algorithm": "sha256"}, "depends_on": ["step_2"], "output_key": null}, {"step_id": "step_4", "tool_name": "send_email", "arguments": {"to": "xander@devops.ci", "subject": "KB Answer: What is the tallest mountain?", "body": "Based on available knowledge, What is the tallest mountain? relates to a topic with multiple facets. Further research may be needed for a complete answer. (EL translation)"}, "depends_on": ["step_2"], "output_key": null}], "final_answer_source": "step_4"} | {"status": "sent", "message_id": "msg_5fe5276cd815", "to": "xander@devops.ci", "subject": "KB Answer: What is the tallest mountain?", "body_preview": "Based on available knowledge, What is the tallest mountain? relates to a topic with multiple facets....", "cc": null, "timestamp": "2026-02-22T12:00:00"} | 4 | 7 | dag | chain-fan-out |
L3_dag_0184 | L3_dag | Answer "Who invented the telephone?" from the knowledge base, translate to Malay, then both hash the translated text and email peter@support.help at the same time. | [
"knowledge_base_query",
"translate_text",
"hash_text",
"send_email",
"number_to_text",
"round_number",
"calculator"
] | {"steps": [{"step_id": "step_1", "tool_name": "knowledge_base_query", "arguments": {"query": "Who invented the telephone?"}, "depends_on": [], "output_key": "kb_result"}, {"step_id": "step_2", "tool_name": "translate_text", "arguments": {"text": "Based on available knowledge, Who invented the telephone? relates to a topic with multiple facets. Further research may be needed for a complete answer.", "from_language": "en", "to_language": "ms"}, "depends_on": ["step_1"], "output_key": "translated"}, {"step_id": "step_3", "tool_name": "hash_text", "arguments": {"text": "Based on available knowledge, Who invented the telephone? relates to a topic with multiple facets. Further research may be needed for a complete answer. (MS translation)", "algorithm": "sha256"}, "depends_on": ["step_2"], "output_key": null}, {"step_id": "step_4", "tool_name": "send_email", "arguments": {"to": "peter@support.help", "subject": "KB Answer: Who invented the telephone?", "body": "Based on available knowledge, Who invented the telephone? relates to a topic with multiple facets. Further research may be needed for a complete answer. (MS translation)"}, "depends_on": ["step_2"], "output_key": null}], "final_answer_source": "step_4"} | {"status": "sent", "message_id": "msg_6df01e4e08fd", "to": "peter@support.help", "subject": "KB Answer: Who invented the telephone?", "body_preview": "Based on available knowledge, Who invented the telephone? relates to a topic with multiple facets. F...", "cc": null, "timestamp": "2026-02-22T12:00:00"} | 4 | 7 | dag | chain-fan-out |
L3_dag_0185 | L3_dag | Query the KB for "What is the deepest ocean trench?", translate the response to Turkish, and simultaneously compute its hash and send it via email to grace@hospital.med. | [
"knowledge_base_query",
"translate_text",
"hash_text",
"send_email",
"create_calendar_event",
"web_search",
"detect_language"
] | {"steps": [{"step_id": "step_1", "tool_name": "knowledge_base_query", "arguments": {"query": "What is the deepest ocean trench?"}, "depends_on": [], "output_key": "kb_result"}, {"step_id": "step_2", "tool_name": "translate_text", "arguments": {"text": "Based on available knowledge, What is the deepest ocean trench? relates to a topic with multiple facets. Further research may be needed for a complete answer.", "from_language": "en", "to_language": "tr"}, "depends_on": ["step_1"], "output_key": "translated"}, {"step_id": "step_3", "tool_name": "hash_text", "arguments": {"text": "Based on available knowledge, What is the deepest ocean trench? relates to a topic with multiple facets. Further research may be needed for a complete answer. (TR translation)", "algorithm": "sha256"}, "depends_on": ["step_2"], "output_key": null}, {"step_id": "step_4", "tool_name": "send_email", "arguments": {"to": "grace@hospital.med", "subject": "KB Answer: What is the deepest ocean trench?", "body": "Based on available knowledge, What is the deepest ocean trench? relates to a topic with multiple facets. Further research may be needed for a complete answer. (TR translation)"}, "depends_on": ["step_2"], "output_key": null}], "final_answer_source": "step_4"} | {"status": "sent", "message_id": "msg_c55653e11daa", "to": "grace@hospital.med", "subject": "KB Answer: What is the deepest ocean trench?", "body_preview": "Based on available knowledge, What is the deepest ocean trench? relates to a topic with multiple fac...", "cc": null, "timestamp": "2026-02-22T12:00:00"} | 4 | 7 | dag | chain-fan-out |
L3_dag_0186 | L3_dag | Open files /data/employees.csv and /data/report.txt simultaneously, get word counts for both, compute the percent change, and log it. | [
"read_file",
"word_count",
"percentage_change",
"log_event",
"lookup_entity",
"translate_text",
"get_weekday"
] | {"steps": [{"step_id": "step_1", "tool_name": "read_file", "arguments": {"path": "/data/employees.csv"}, "depends_on": [], "output_key": "f1"}, {"step_id": "step_2", "tool_name": "read_file", "arguments": {"path": "/data/report.txt"}, "depends_on": [], "output_key": "f2"}, {"step_id": "step_3", "tool_name": "word_count", "arguments": {"text": "name,department,salary,years\nAlice,Engineering,95000,5\nBob,Marketing,72000,3\nCharlie,Engineering,105000,8\nDiana,Sales,68000,2\nEve,Engineering,88000,4\nFrank,Marketing,76000,6\nGrace,Sales,71000,3\nHenry,Engineering,112000,10"}, "depends_on": ["step_1"], "output_key": "wc1"}, {"step_id": "step_4", "tool_name": "word_count", "arguments": {"text": "Q1 2026 Sales Report\n\nTotal revenue: $2.4M\nGrowth: 15% YoY\nTop product: Enterprise Plan ($1.2M)\nNew customers: 340\nChurn rate: 2.1%\n\nKey highlights:\n- Enterprise segment grew 28%\n- APAC region exceeded targets by 12%\n- Customer satisfaction score: 4.6/5.0"}, "depends_on": ["step_2"], "output_key": "wc2"}, {"step_id": "step_5", "tool_name": "percentage_change", "arguments": {"old_value": 9, "new_value": 40}, "depends_on": ["step_3", "step_4"], "output_key": null}, {"step_id": "step_6", "tool_name": "log_event", "arguments": {"event_type": "word_count_comparison", "message": "Word count comparison: /data/employees.csv has 9 words, /data/report.txt has 40 words. Change: 344.4%", "severity": "info"}, "depends_on": ["step_5"], "output_key": null}], "final_answer_source": "step_6"} | {"log_id": "log_9d0c972b6e76", "event_type": "word_count_comparison", "message": "Word count comparison: /data/employees.csv has 9 words, /data/report.txt has 40 words. Change: 344.4%", "severity": "info", "timestamp": "2026-02-22T12:00:00"} | 6 | 7 | dag | true-dag-parallel-reads-merge |
L3_dag_0187 | L3_dag | Answer "What is the greenhouse effect?" from the knowledge base, translate to Polish, then both hash the translated text and email charlie@startup.dev at the same time. | [
"knowledge_base_query",
"translate_text",
"hash_text",
"send_email",
"list_files",
"base64_encode",
"json_extract"
] | {"steps": [{"step_id": "step_1", "tool_name": "knowledge_base_query", "arguments": {"query": "What is the greenhouse effect?"}, "depends_on": [], "output_key": "kb_result"}, {"step_id": "step_2", "tool_name": "translate_text", "arguments": {"text": "Based on available knowledge, What is the greenhouse effect? relates to a topic with multiple facets. Further research may be needed for a complete answer.", "from_language": "en", "to_language": "pl"}, "depends_on": ["step_1"], "output_key": "translated"}, {"step_id": "step_3", "tool_name": "hash_text", "arguments": {"text": "Based on available knowledge, What is the greenhouse effect? relates to a topic with multiple facets. Further research may be needed for a complete answer. (PL translation)", "algorithm": "sha256"}, "depends_on": ["step_2"], "output_key": null}, {"step_id": "step_4", "tool_name": "send_email", "arguments": {"to": "charlie@startup.dev", "subject": "KB Answer: What is the greenhouse effect?", "body": "Based on available knowledge, What is the greenhouse effect? relates to a topic with multiple facets. Further research may be needed for a complete answer. (PL translation)"}, "depends_on": ["step_2"], "output_key": null}], "final_answer_source": "step_4"} | {"status": "sent", "message_id": "msg_f9ece757cb04", "to": "charlie@startup.dev", "subject": "KB Answer: What is the greenhouse effect?", "body_preview": "Based on available knowledge, What is the greenhouse effect? relates to a topic with multiple facets...", "cc": null, "timestamp": "2026-02-22T12:00:00"} | 4 | 7 | dag | chain-fan-out |
L3_dag_0188 | L3_dag | Extract entities from two texts, combine the results, mask any PII, and save the cleaned text to /data/pii_cleaned.txt. | [
"extract_entities",
"mask_pii",
"write_file",
"parse_date",
"encode_url",
"sentiment_analysis"
] | {"steps": [{"step_id": "step_1", "tool_name": "extract_entities", "arguments": {"text": "Contact Dr. Sarah Johnson at sjohnson@university.edu about the meeting on 03/15/2026."}, "depends_on": [], "output_key": "ent1"}, {"step_id": "step_2", "tool_name": "extract_entities", "arguments": {"text": "Albert Einstein was born in Ulm, Germany on March 14, 1879."}, "depends_on": [], "output_key": "ent2"}, {"step_id": "step_3", "tool_name": "mask_pii", "arguments": {"text": "Contact Dr. Sarah Johnson at sjohnson@university.edu about the meeting on 03/15/2026.\n---\nAlbert Einstein was born in Ulm, Germany on March 14, 1879."}, "depends_on": ["step_1", "step_2"], "output_key": null}, {"step_id": "step_4", "tool_name": "write_file", "arguments": {"path": "/data/pii_cleaned.txt", "content": "{\"entities_text1\": [{\"text\": \"sjohnson@university.edu\", \"type\": \"email\"}, {\"text\": \"03/15/2026\", \"type\": \"date\"}, {\"text\": \"03\", \"type\": \"number\"}, {\"text\": \"15\", \"type\": \"number\"}, {\"text\": \"Contact Dr\", \"type\": \"capitalized_phrase\"}, {\"text\": \"Sarah Johnson\", \"type\": \"capitalized_phrase\"}], \"entities_text2\": [{\"text\": \"March 14, 1879\", \"type\": \"date\"}, {\"text\": \"14\", \"type\": \"number\"}, {\"text\": \"Albert Einstein\", \"type\": \"capitalized_phrase\"}], \"masked_text\": \"Contact Dr. Sarah Johnson at [EMAIL] about the meeting on 03/15/2026.\\n---\\nAlbert Einstein was born in Ulm, Germany on March 14, 1879.\"}"}, "depends_on": ["step_3"], "output_key": null}], "final_answer_source": "step_4"} | {"status": "written", "path": "/data/pii_cleaned.txt", "size": 604, "type": "text/plain", "overwrote_existing": true} | 4 | 6 | dag | parallel-process-save |
L3_dag_0189 | L3_dag | Find named entities in two different texts, then mask personally identifiable information from the combined text and write it to /data/entities_masked.txt. | [
"extract_entities",
"mask_pii",
"write_file",
"rss_feed_parse",
"next_occurrence",
"encode_url"
] | {"steps": [{"step_id": "step_1", "tool_name": "extract_entities", "arguments": {"text": "Elon Musk founded SpaceX in 2002 and Tesla headquarters is in Austin, Texas."}, "depends_on": [], "output_key": "ent1"}, {"step_id": "step_2", "tool_name": "extract_entities", "arguments": {"text": "Final exam schedule: email registrar@university.edu or visit room 301 in Admin Building."}, "depends_on": [], "output_key": "ent2"}, {"step_id": "step_3", "tool_name": "mask_pii", "arguments": {"text": "Elon Musk founded SpaceX in 2002 and Tesla headquarters is in Austin, Texas.\n---\nFinal exam schedule: email registrar@university.edu or visit room 301 in Admin Building."}, "depends_on": ["step_1", "step_2"], "output_key": null}, {"step_id": "step_4", "tool_name": "write_file", "arguments": {"path": "/data/entities_masked.txt", "content": "{\"entities_text1\": [{\"text\": \"Elon Musk\", \"type\": \"capitalized_phrase\"}], \"entities_text2\": [{\"text\": \"registrar@university.edu\", \"type\": \"email\"}, {\"text\": \"301\", \"type\": \"number\"}, {\"text\": \"Admin Building\", \"type\": \"capitalized_phrase\"}], \"masked_text\": \"Elon Musk founded SpaceX in 2002 and Tesla headquarters is in Austin, Texas.\\n---\\nFinal exam schedule: email [EMAIL] or visit room 301 in Admin Building.\"}"}, "depends_on": ["step_3"], "output_key": null}], "final_answer_source": "step_4"} | {"status": "written", "path": "/data/entities_masked.txt", "size": 414, "type": "text/plain", "overwrote_existing": false} | 4 | 6 | dag | parallel-process-save |
L3_dag_0190 | L3_dag | Extract entities from two texts, combine the results, mask any PII, and save the cleaned text to /data/entities_masked.txt. | [
"extract_entities",
"mask_pii",
"write_file",
"list_memories",
"database_query",
"generate_url"
] | {"steps": [{"step_id": "step_1", "tool_name": "extract_entities", "arguments": {"text": "Contact Dr. Sarah Johnson at sjohnson@university.edu about the meeting on 03/15/2026."}, "depends_on": [], "output_key": "ent1"}, {"step_id": "step_2", "tool_name": "extract_entities", "arguments": {"text": "Contact hr@globalinc.com to schedule an interview. The position is based in London."}, "depends_on": [], "output_key": "ent2"}, {"step_id": "step_3", "tool_name": "mask_pii", "arguments": {"text": "Contact Dr. Sarah Johnson at sjohnson@university.edu about the meeting on 03/15/2026.\n---\nContact hr@globalinc.com to schedule an interview. The position is based in London."}, "depends_on": ["step_1", "step_2"], "output_key": null}, {"step_id": "step_4", "tool_name": "write_file", "arguments": {"path": "/data/entities_masked.txt", "content": "{\"entities_text1\": [{\"text\": \"sjohnson@university.edu\", \"type\": \"email\"}, {\"text\": \"03/15/2026\", \"type\": \"date\"}, {\"text\": \"03\", \"type\": \"number\"}, {\"text\": \"15\", \"type\": \"number\"}, {\"text\": \"Contact Dr\", \"type\": \"capitalized_phrase\"}, {\"text\": \"Sarah Johnson\", \"type\": \"capitalized_phrase\"}], \"entities_text2\": [{\"text\": \"hr@globalinc.com\", \"type\": \"email\"}], \"masked_text\": \"Contact Dr. Sarah Johnson at [EMAIL] about the meeting on 03/15/2026.\\n---\\nContact [EMAIL] to schedule an interview. The position is based in London.\"}"}, "depends_on": ["step_3"], "output_key": null}], "final_answer_source": "step_4"} | {"status": "written", "path": "/data/entities_masked.txt", "size": 529, "type": "text/plain", "overwrote_existing": true} | 4 | 6 | dag | parallel-process-save |
L3_dag_0191 | L3_dag | Convert 250 USD to THB, MXN, and EUR simultaneously, find the min and max converted values, and format the result. | [
"get_exchange_rate",
"min_max",
"format_number",
"web_page_fetch",
"unit_convert",
"parse_date"
] | {"steps": [{"step_id": "step_1", "tool_name": "get_exchange_rate", "arguments": {"from_currency": "USD", "to_currency": "THB", "amount": 250}, "depends_on": [], "output_key": "conv1"}, {"step_id": "step_2", "tool_name": "get_exchange_rate", "arguments": {"from_currency": "USD", "to_currency": "MXN", "amount": 250}, "depends_on": [], "output_key": "conv2"}, {"step_id": "step_3", "tool_name": "get_exchange_rate", "arguments": {"from_currency": "USD", "to_currency": "EUR", "amount": 250}, "depends_on": [], "output_key": "conv3"}, {"step_id": "step_4", "tool_name": "min_max", "arguments": {"numbers": [250.0, 4287.5, 230.0]}, "depends_on": ["step_1", "step_2", "step_3"], "output_key": null}, {"step_id": "step_5", "tool_name": "format_number", "arguments": {"value": 4287.5, "format": "comma"}, "depends_on": ["step_4"], "output_key": null}], "final_answer_source": "step_5"} | {"formatted": "4,287.50"} | 5 | 6 | dag | fan-out-aggregate-format |
L3_dag_0192 | L3_dag | Search for "smart city infrastructure", extract entities and analyze sentiment in parallel, generate a report, and email it to quinn@analytics.data. | [
"web_search",
"extract_entities",
"sentiment_analysis",
"generate_report",
"send_email",
"dns_lookup",
"execute_python",
"create_invoice"
] | {"steps": [{"step_id": "step_1", "tool_name": "web_search", "arguments": {"query": "smart city infrastructure"}, "depends_on": [], "output_key": "results"}, {"step_id": "step_2", "tool_name": "extract_entities", "arguments": {"text": "[{\"title\": \"Smart City Infrastructure \\u2014 News Overview\", \"url\": \"https://stackoverflow.com/article/32326\", \"snippet\": \"Latest research findings on smart city infrastructure from leading institutions and peer-reviewed publications.\", \"source\": \"News\"}, {\"title\": \"Smart City Infrastructure \\u2014 Forum Analysis\", \"url\": \"https://medium.com/article/13104\", \"snippet\": \"Comparing different approaches to smart city infrastructure \\u2014 strengths, limitations, and real-world performance.\", \"source"}, "depends_on": ["step_1"], "output_key": "entities"}, {"step_id": "step_3", "tool_name": "sentiment_analysis", "arguments": {"text": "[{\"title\": \"Smart City Infrastructure \\u2014 News Overview\", \"url\": \"https://stackoverflow.com/article/32326\", \"snippet\": \"Latest research findings on smart city infrastructure from leading institutions and peer-reviewed publications.\", \"source\": \"News\"}, {\"title\": \"Smart City Infrastructure \\u2014 Forum Analysis\", \"url\": \"https://medium.com/article/13104\", \"snippet\": \"Comparing different approaches to smart city infrastructure \\u2014 strengths, limitations, and real-world performance.\", \"source"}, "depends_on": ["step_1"], "output_key": "sentiment"}, {"step_id": "step_4", "tool_name": "generate_report", "arguments": {"title": "Analysis Report: smart city infrastructure", "data": "{\"query\": \"smart city infrastructure\", \"entities_found\": 5, \"top_entities\": [\"https://stackoverflow.com/article/32326\", \"https://medium.com/article/13104\", \"Smart City Infrastructure\", \"News Overview\", \"Forum Analysis\"], \"sentiment\": \"neutral\", \"sentiment_confidence\": 0.5}", "format": "text"}, "depends_on": ["step_2", "step_3"], "output_key": "report"}, {"step_id": "step_5", "tool_name": "send_email", "arguments": {"to": "quinn@analytics.data", "subject": "Analysis: smart city infrastructure", "body": "ANALYSIS REPORT: SMART CITY INFRASTRUCTURE\n==========================================\n\nSummary:\n query: smart city infrastructure\n entities_found: 5\n top_entities: ['https://stackoverflow.com/article/32326', 'https://medium.com/article/13104', 'Smart City Infrastructure', 'News Overview', 'Forum Analysis']\n sentiment: neutral\n sentiment_confidence: 0.5\n\nGenerated: 2026-02-22T12:00:00"}, "depends_on": ["step_4"], "output_key": null}], "final_answer_source": "step_5"} | {"status": "sent", "message_id": "msg_10753c8f717f", "to": "quinn@analytics.data", "subject": "Analysis: smart city infrastructure", "body_preview": "ANALYSIS REPORT: SMART CITY INFRASTRUCTURE\n==========================================\n\nSummary:\n qu...", "cc": null, "timestamp": "2026-02-22T12:00:00"} | 5 | 8 | dag | chain-fanout-merge-chain |
L3_dag_0193 | L3_dag | Look up V and ORCL stocks, find the percentage difference, and notify about the comparison. | [
"get_stock_price",
"percentage_change",
"create_notification",
"get_current_time",
"is_business_day",
"set_reminder"
] | {"steps": [{"step_id": "step_1", "tool_name": "get_stock_price", "arguments": {"symbol": "V"}, "depends_on": [], "output_key": "s1"}, {"step_id": "step_2", "tool_name": "get_stock_price", "arguments": {"symbol": "ORCL"}, "depends_on": [], "output_key": "s2"}, {"step_id": "step_3", "tool_name": "percentage_change", "arguments": {"old_value": 113, "new_value": 85}, "depends_on": ["step_1", "step_2"], "output_key": null}, {"step_id": "step_4", "tool_name": "create_notification", "arguments": {"title": "Stock Comparison", "message": "ORCL is -24.8% decrease compared to V", "priority": "medium"}, "depends_on": ["step_3"], "output_key": null}], "final_answer_source": "step_4"} | {"status": "created", "notification_id": "notif_787053e1eddf", "title": "Stock Comparison", "priority": "medium", "timestamp": "2026-02-22T12:00:00"} | 4 | 6 | dag | parallel-compute-notify |
L3_dag_0194 | L3_dag | Look up Rome's weather, create a Incident Postmortem event on 2026-04-15 10:00, and set a reminder about the event. | [
"get_weather",
"create_calendar_event",
"set_reminder",
"word_count",
"extract_domain",
"hash_text",
"store_memory"
] | {"steps": [{"step_id": "step_1", "tool_name": "get_weather", "arguments": {"city": "Rome"}, "depends_on": [], "output_key": "weather"}, {"step_id": "step_2", "tool_name": "create_calendar_event", "arguments": {"title": "Incident Postmortem (Outdoor - windy)", "date": "2026-04-15 10:00", "duration_minutes": 60}, "depends_on": ["step_1"], "output_key": "event"}, {"step_id": "step_3", "tool_name": "set_reminder", "arguments": {"message": "Reminder: Incident Postmortem in Rome. Weather in Rome: 24°C, windy", "remind_at": "2026-04-15 10:00"}, "depends_on": ["step_2"], "output_key": "reminder"}, {"step_id": "step_4", "tool_name": "word_count", "arguments": {"text": "Weather in Rome: 24°C, windy"}, "depends_on": ["step_1"], "output_key": null}], "final_answer_source": "step_3"} | {"reminder_id": "rem_7bb92981df1e", "message": "Reminder: Incident Postmortem in Rome. Weather in Rome: 24°C, windy", "remind_at": "2026-04-15 10:00", "confirmation": "Reminder set for 2026-04-15 10:00: 'Reminder: Incident Postmortem in Rome. Weather in Rome: 24°C, windy'."} | 4 | 7 | dag | chain-with-branch |
L3_dag_0195 | L3_dag | Read the file at /data/employees.csv, extract all numbers, compute statistics, and save the results to /data/numbers_report.json. | [
"read_file",
"extract_numbers",
"statistical_analysis",
"write_file",
"data_sort",
"log_event",
"text_to_number"
] | {"steps": [{"step_id": "step_1", "tool_name": "read_file", "arguments": {"path": "/data/employees.csv"}, "depends_on": [], "output_key": "file_data"}, {"step_id": "step_2", "tool_name": "extract_numbers", "arguments": {"text": "name,department,salary,years\nAlice,Engineering,95000,5\nBob,Marketing,72000,3\nCharlie,Engineering,105000,8\nDiana,Sales,68000,2\nEve,Engineering,88000,4\nFrank,Marketing,76000,6\nGrace,Sales,71000,3\nHenry,Engineering,112000,10"}, "depends_on": ["step_1"], "output_key": "extracted"}, {"step_id": "step_3", "tool_name": "statistical_analysis", "arguments": {"numbers": [950, 0, 5, 720, 0, 3, 105, 0, 8, 680, 0, 2, 880, 0, 4, 760, 0, 6, 710, 0, 3, 112, 0, 10]}, "depends_on": ["step_2"], "output_key": "stats"}, {"step_id": "step_4", "tool_name": "write_file", "arguments": {"path": "/data/numbers_report.json", "content": "{\"source_file\": \"/data/employees.csv\", \"numbers\": [950, 0, 5, 720, 0, 3, 105, 0, 8, 680, 0, 2, 880, 0, 4, 760, 0, 6, 710, 0, 3, 112, 0, 10], \"statistics\": {\"count\": 24, \"mean\": 206.5833, \"median\": 4.5, \"min\": 0.0, \"max\": 950.0, \"sum\": 4958.0, \"stdev\": 345.0893}}"}, "depends_on": ["step_3"], "output_key": null}], "final_answer_source": "step_4"} | {"status": "written", "path": "/data/numbers_report.json", "size": 262, "type": "application/json", "overwrote_existing": true} | 4 | 7 | dag | linear-chain-4-file-stats |
L3_dag_0196 | L3_dag | Figure out what language "Microplastics have been found in every environment on Earth...." is in, translate to English, create a summary, then send it to diana@university.edu. | [
"detect_language",
"translate_text",
"summarize_text",
"send_email",
"case_convert",
"create_calendar_event",
"data_aggregate"
] | {"steps": [{"step_id": "step_1", "tool_name": "detect_language", "arguments": {"text": "Microplastics have been found in every environment on Earth. Research into their health effects is intensifying."}, "depends_on": [], "output_key": "detected"}, {"step_id": "step_2", "tool_name": "translate_text", "arguments": {"text": "Microplastics have been found in every environment on Earth. Research into their health effects is intensifying.", "from_language": "en", "to_language": "en"}, "depends_on": ["step_1"], "output_key": "translated"}, {"step_id": "step_3", "tool_name": "summarize_text", "arguments": {"text": "Microplastics have been found in every environment on Earth. Research into their health effects is intensifying. (English translation)"}, "depends_on": ["step_2"], "output_key": "summary"}, {"step_id": "step_4", "tool_name": "send_email", "arguments": {"to": "diana@university.edu", "subject": "Translated Summary", "body": "Microplastics have been found in every environment on Earth. Research into their health effects is intensifying. (English translation)"}, "depends_on": ["step_3"], "output_key": null}], "final_answer_source": "step_4"} | {"status": "sent", "message_id": "msg_a200eb5e6d58", "to": "diana@university.edu", "subject": "Translated Summary", "body_preview": "Microplastics have been found in every environment on Earth. Research into their health effects is i...", "cc": null, "timestamp": "2026-02-22T12:00:00"} | 4 | 7 | dag | linear-chain-4-nlp |
L3_dag_0197 | L3_dag | Open files /data/products.json and /data/employees.csv simultaneously, get word counts for both, compute the percent change, and log it. | [
"read_file",
"word_count",
"percentage_change",
"log_event",
"convert_timezone",
"retrieve_memory",
"min_max"
] | {"steps": [{"step_id": "step_1", "tool_name": "read_file", "arguments": {"path": "/data/products.json"}, "depends_on": [], "output_key": "f1"}, {"step_id": "step_2", "tool_name": "read_file", "arguments": {"path": "/data/employees.csv"}, "depends_on": [], "output_key": "f2"}, {"step_id": "step_3", "tool_name": "word_count", "arguments": {"text": "[{\"id\": 1, \"name\": \"Basic Plan\", \"price\": 29, \"category\": \"subscription\"}, {\"id\": 2, \"name\": \"Pro Plan\", \"price\": 79, \"category\": \"subscription\"}, {\"id\": 3, \"name\": \"Enterprise Plan\", \"price\": 199, \"category\": \"subscription\"}, {\"id\": 4, \"name\": \"API Access\", \"price\": 49, \"category\": \"addon\"}, {\"id\": 5, \"name\": \"Premium Support\", \"price\": 99, \"category\": \"addon\"}]"}, "depends_on": ["step_1"], "output_key": "wc1"}, {"step_id": "step_4", "tool_name": "word_count", "arguments": {"text": "name,department,salary,years\nAlice,Engineering,95000,5\nBob,Marketing,72000,3\nCharlie,Engineering,105000,8\nDiana,Sales,68000,2\nEve,Engineering,88000,4\nFrank,Marketing,76000,6\nGrace,Sales,71000,3\nHenry,Engineering,112000,10"}, "depends_on": ["step_2"], "output_key": "wc2"}, {"step_id": "step_5", "tool_name": "percentage_change", "arguments": {"old_value": 45, "new_value": 9}, "depends_on": ["step_3", "step_4"], "output_key": null}, {"step_id": "step_6", "tool_name": "log_event", "arguments": {"event_type": "word_count_comparison", "message": "Word count comparison: /data/products.json has 45 words, /data/employees.csv has 9 words. Change: -80.0%", "severity": "info"}, "depends_on": ["step_5"], "output_key": null}], "final_answer_source": "step_6"} | {"log_id": "log_0fabdbfbc848", "event_type": "word_count_comparison", "message": "Word count comparison: /data/products.json has 45 words, /data/employees.csv has 9 words. Change: -80.0%", "severity": "info", "timestamp": "2026-02-22T12:00:00"} | 6 | 7 | dag | true-dag-parallel-reads-merge |
L3_dag_0198 | L3_dag | Get the prices of UBER and PINS, compute the percent change between them, and create a notification with the result. | [
"get_stock_price",
"percentage_change",
"create_notification",
"add_duration",
"extract_domain",
"truncate_text"
] | {"steps": [{"step_id": "step_1", "tool_name": "get_stock_price", "arguments": {"symbol": "UBER"}, "depends_on": [], "output_key": "s1"}, {"step_id": "step_2", "tool_name": "get_stock_price", "arguments": {"symbol": "PINS"}, "depends_on": [], "output_key": "s2"}, {"step_id": "step_3", "tool_name": "percentage_change", "arguments": {"old_value": 133, "new_value": 236}, "depends_on": ["step_1", "step_2"], "output_key": null}, {"step_id": "step_4", "tool_name": "create_notification", "arguments": {"title": "Stock Comparison", "message": "PINS is 77.4% increase compared to UBER", "priority": "medium"}, "depends_on": ["step_3"], "output_key": null}], "final_answer_source": "step_4"} | {"status": "created", "notification_id": "notif_787053e1eddf", "title": "Stock Comparison", "priority": "medium", "timestamp": "2026-02-22T12:00:00"} | 4 | 6 | dag | parallel-compute-notify |
L3_dag_0199 | L3_dag | Exchange 2500 USD into SAR, CNY, and NZD in parallel, determine which gives the highest and lowest amounts, and format the output. | [
"get_exchange_rate",
"min_max",
"format_number",
"transform_format",
"text_to_number",
"standard_deviation"
] | {"steps": [{"step_id": "step_1", "tool_name": "get_exchange_rate", "arguments": {"from_currency": "USD", "to_currency": "SAR", "amount": 2500}, "depends_on": [], "output_key": "conv1"}, {"step_id": "step_2", "tool_name": "get_exchange_rate", "arguments": {"from_currency": "USD", "to_currency": "CNY", "amount": 2500}, "depends_on": [], "output_key": "conv2"}, {"step_id": "step_3", "tool_name": "get_exchange_rate", "arguments": {"from_currency": "USD", "to_currency": "NZD", "amount": 2500}, "depends_on": [], "output_key": "conv3"}, {"step_id": "step_4", "tool_name": "min_max", "arguments": {"numbers": [2500.0, 18100.0, 2500.0]}, "depends_on": ["step_1", "step_2", "step_3"], "output_key": null}, {"step_id": "step_5", "tool_name": "format_number", "arguments": {"value": 18100.0, "format": "comma"}, "depends_on": ["step_4"], "output_key": null}], "final_answer_source": "step_5"} | {"formatted": "18,100.00"} | 5 | 6 | dag | fan-out-aggregate-format |
L3_dag_0200 | L3_dag | Answer "Who was the first person to walk on the Moon?" from the knowledge base, translate to Dutch, then both hash the translated text and email nick@marketing.brand at the same time. | [
"knowledge_base_query",
"translate_text",
"hash_text",
"send_email",
"add_duration",
"linear_regression",
"mask_pii"
] | {"steps": [{"step_id": "step_1", "tool_name": "knowledge_base_query", "arguments": {"query": "Who was the first person to walk on the Moon?"}, "depends_on": [], "output_key": "kb_result"}, {"step_id": "step_2", "tool_name": "translate_text", "arguments": {"text": "Based on available knowledge, Who was the first person to walk on the Moon? relates to a topic with multiple facets. Further research may be needed for a complete answer.", "from_language": "en", "to_language": "nl"}, "depends_on": ["step_1"], "output_key": "translated"}, {"step_id": "step_3", "tool_name": "hash_text", "arguments": {"text": "Based on available knowledge, Who was the first person to walk on the Moon? relates to a topic with multiple facets. Further research may be needed for a complete answer. (NL translation)", "algorithm": "sha256"}, "depends_on": ["step_2"], "output_key": null}, {"step_id": "step_4", "tool_name": "send_email", "arguments": {"to": "nick@marketing.brand", "subject": "KB Answer: Who was the first person to walk on the Moon?", "body": "Based on available knowledge, Who was the first person to walk on the Moon? relates to a topic with multiple facets. Further research may be needed for a complete answer. (NL translation)"}, "depends_on": ["step_2"], "output_key": null}], "final_answer_source": "step_4"} | {"status": "sent", "message_id": "msg_390407b45e8a", "to": "nick@marketing.brand", "subject": "KB Answer: Who was the first person to walk on the Moon?", "body_preview": "Based on available knowledge, Who was the first person to walk on the Moon? relates to a topic with ...", "cc": null, "timestamp": "2026-02-22T12:00:00"} | 4 | 7 | dag | chain-fan-out |
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