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"... | {"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... | {"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": {... | {"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":... | {"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... | {"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/... | {"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": "w... | {"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": "writ... | {"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": "writ... | {"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": "{}"}... | {"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": "{}"}, ... | {"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", "para... | {"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": ... | {"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_wea... | {"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": ... | {"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_s... | {"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... | {"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_p... | {"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 relea... | [{"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_si... | 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 recom... | [{"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_s... | 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 ... | [{"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_sig... | 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"}], "f... | {"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": "N... | 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"}],... | {"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": "Ac... | 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"}], "... | {"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"}, {"tit... | 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... | {"sha256": {"hash": "d8162bb5be5346662286285b590cf03b9186f1441fefb0acd7a05391c3193827", "algorithm": "sha256"}, "md5": {"hash": "b450f107966a87400de4ceb277024fa8", "algorithm": "md5"}, "sha512": {"hash": "a399c26195b2a3899d62c73d16cdb0bec05b6a197a84134ea4041b67eb66db03924d30de4eb01c869acc391c19130da36dbe28eef7fb8e32e63... | 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"}, {"ste... | {"sha256": {"hash": "ad04cbd772f24a471bef86e3163442c46760eea9c429ec7018e7ced62165be85", "algorithm": "sha256"}, "md5": {"hash": "9f050aa18db80159f7cb909d4e6d912f", "algorithm": "md5"}, "sha512": {"hash": "78b662d103429340dbe1eb3b3cdc1bc502f388a142cedb20f58445b7c5267bcb6a0027c9567bf744d109a60c82bfad53626e81c5cad2d55e549... | 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", "... | {"sha256": {"hash": "36609d114a61067a13e8a2a7369d97e01bf0a20e7cf8e5eea0974b7fa2109c44", "algorithm": "sha256"}, "md5": {"hash": "098f59b77ea50480cbed8694b7035024", "algorithm": "md5"}, "sha512": {"hash": "920c28253ecc7b71d7e9ad2492bf7e70177bec29ed1aae22ffcfaece192aa01a0942baf5c3389bf18710acd043f8238affd09bbf7ccb82f41b8... | 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"... | {"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": 2... | 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"... | {"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, "a... | 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":... | {"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": 1... | 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"}, {"ste... | {"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"}, {"ste... | {"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"}, {"ste... | {"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_extrac... | [{"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, ... | 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_extr... | [{"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"], ... | 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", "... | [{"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.... | 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... | [{"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 equ... | [{"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 woh... | [{"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": {"... | [{"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", "argument... | [{"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... | [{"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 feedb... | [{"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... | [{"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 orientat... | [{"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_timesta... | 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... | 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... | 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"}, "depend... | {"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"... | {"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", "argumen... | {"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":... | {"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,Engi... | {"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... | {"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,Engi... | {"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\", ... | {"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 Anal... | 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:/... | {"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 Analysi... | 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\": ... | {"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 Bl... | 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", "arg... | {"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", ... | {"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": "transl... | {"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", "arg... | {"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", "a... | {"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", "ar... | {"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}, "depe... | {"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}, "d... | {"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":... | {"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 Princip... | {"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 tele... | {"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... | {"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",... | {"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_coun... | {"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... | {"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\": \"htt... | {"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 Ove... | {"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\": \"ht... | {"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}, "de... | {"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}, "de... | {"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}, "de... | {"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 ... | {"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 to... | {"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? rel... | {"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... | {"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 t... | {"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 bor... | {"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 regis... | {"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.co... | {"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}, "de... | {"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://... | {"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", "arg... | {"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}, "depend... | {"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,Engi... | {"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": {"tex... | {"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_co... | {"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", "... | {"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}, "... | {"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 ... | {"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:... | 4 | 7 | dag | chain-fan-out |
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