API Key: ✅ Set Model: gpt-5-mini Database: financial_data.db Status: 🟢 Ready Verbose Mode: ✅ Enabled
🚀 System ready! Type your queries below. 💡 Type 'exit', 'quit', or press Ctrl+C to stop. 🧠 Chat history: Enabled (max 20 messages, SQLite persistence)
[Query #1] 💬 Enter your request: show me the schema of the db
🔄 ID #session_20250730_165349
🔄 Processing: 'show me the schema of the db' ⏳ Working... [supervisor_1] INFO: Input validation completed successfully. [interpreter] INFO: Input validation passed for Interpreter Agent. [data_prep] INFO: Input validation completed successfully.
Tool info: sql_db_query: Input to this tool is a detailed and correct SQL query, output is a result from the database. If the query is not correct, an error message will be returned. If an error is returned, rewrite the query, check the query, and try again. If you encounter an issue with Unknown column 'xxxx' in 'field list', use sql_db_schema to query the correct table fields. sql_db_schema: Input to this tool is a comma-separated list of tables, output is the schema and sample rows for those tables. Be sure that the tables actually exist by calling sql_db_list_tables first! Example Input: table1, table2, table3 sql_db_list_tables: Input is an empty string, output is a comma-separated list of tables in the database. sql_db_query_checker: Use this tool to double check if your query is correct before executing it. Always use this tool before executing a query with sql_db_query!python_code_sandbox: A secure Python code sandbox. Use this to execute python commands.
- Input should be a valid python command.
- To return output, you should print it out with
print(...). - Don't use f-strings when printing outputs.
- If you need to make web requests, use
httpx.AsyncClient. [data_prep] INFO: Creating workflow for data preparation tasks. [supervisor_1] INFO: Executing data preparation tasks. DEBUG: Found 7 messages: [0] HumanMessage | name: None | has_tool_calls: False | is_tool_msg: False [1] AIMessage | name: Supervisor | has_tool_calls: [{'name': 'transfer_to_data_prep', 'args': {}, 'id': 'call_zQAMstXYWoNgUSfrhshvtlP8', 'type': 'tool_call'}] | is_tool_msg: False [2] ToolMessage | name: transfer_to_data_prep | has_tool_calls: False | is_tool_msg: True Tool: transfer_to_data_prep [3] AIMessage | name: data_prep | has_tool_calls: [] | is_tool_msg: False [4] AIMessage | name: data_prep | has_tool_calls: [{'name': 'transfer_back_to_supervisor', 'args': {}, 'id': '057bf94b-7275-4336-8ef6-fb88f5a7557b', 'type': 'tool_call'}] | is_tool_msg: False [5] ToolMessage | name: transfer_back_to_supervisor | has_tool_calls: False | is_tool_msg: True Tool: transfer_back_to_supervisor [6] AIMessage | name: Supervisor | has_tool_calls: [] | is_tool_msg: False DEBUG: Extracted 2 tool calls ╭───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 📊 SUCCESS ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮ │ 🤖 AI Response: │ │ <thought_process> │ │ The user requested the schema of the database. This is a straightforward request to understand the structure of the data. The Data Prep agent was the appropriate choice to retrieve the schema information from the database. The schema includes the table name and all columns │ │ with their data types. The information has been gathered and is ready to be presented to the user. │ │ </thought_process> │ │ │ │ │ │ The database contains one table named "finance_data" with the following schema: │ │ │ │ - Date (TEXT) │ │ - Stock Index (TEXT) │ │ - Open Price (REAL) │ │ - Close Price (REAL) │ │ - Daily High (REAL) │ │ - Daily Low (REAL) │ │ - Trading Volume (INTEGER) │ │ - GDP Growth (%) (REAL) │ │ - Inflation Rate (%) (REAL) │ │ - Unemployment Rate (%) (REAL) │ │ - Interest Rate (%) (REAL) │ │ - Consumer Confidence Index (INTEGER) │ │ - Government Debt (Billion USD) (INTEGER) │ │ - Corporate Profits (Billion USD) (INTEGER) │ │ - Forex USD/EUR (REAL) │ │ - Forex USD/JPY (REAL) │ │ - Crude Oil Price (USD per Barrel) (REAL) │ │ - Gold Price (USD per Ounce) (REAL) │ │ - Real Estate Index (REAL) │ │ - Retail Sales (Billion USD) (INTEGER) │ │ - Bankruptcy Rate (%) (REAL) │ │ - Mergers & Acquisitions Deals (INTEGER) │ │ - Venture Capital Funding (Billion USD) (REAL) │ │ - Consumer Spending (Billion USD) (INTEGER) │ │ │ │ │ │ │ │ 🔄 Agent Collaboration: │ │ Supervisor → Data Prep │ ╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
╭─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 🔧 Tool Calls Details ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮ │ │ │ 🔧 Tool Calls │ │ ┏━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ │ │ ┃ Agent ┃ Tool Name ┃ Purpose ┃ Arguments/Query ┃ Result Preview ┃ │ │ ┡━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ │ │ │ Supervisor │ Transfer To Data Prep │ Delegate to Data Prep │ - │ Successfully transferred to data_prep │ │ │ ├────────────┼─────────────────────────────┼───────────────────────┼─────────────────┼──────────────────────────────────────────┤ │ │ │ Data Prep │ Transfer Back To Supervisor │ Return Control │ - │ Successfully transferred back to │ │ │ │ │ │ │ │ Supervisor │ │ │ └────────────┴─────────────────────────────┴───────────────────────┴─────────────────┴──────────────────────────────────────────┘ │ │ │ ╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
🔄 Agent Flow ├── 👤 User: show me the schema of the db ├── ↔ Transfer To Data Prep ├── 🤖 Data Prep: The database contains one table named "finance_data" with the following schema: │ │ - Date (TEXT) │ - Stock Index (TEXT) │ - Open Price (REAL) │ - Close Price (REAL) │ - Daily High (REAL) │ - Daily Low (REAL) │ - Tr... ├── 🤖 Data Prep: Transferring back to Supervisor └── ↔ Transfer Back To Supervisor
📊 Token Usage ┏━━━━━━━━┳━━━━━━━┓ ┃ Type ┃ Count ┃ ┡━━━━━━━━╇━━━━━━━┩ │ Input │ 697 │ │ Output │ 299 │ │ Total │ 996 │ └────────┴───────┘
📈 Execution Statistics ┏━━━━━━━━━━━━━━━━━━┳━━━━━━━━┓ ┃ Metric ┃ Value ┃ ┡━━━━━━━━━━━━━━━━━━╇━━━━━━━━┩ │ Execution Time │ 20.07s │ │ Agents Involved │ 2 │ │ Total Tool Calls │ 2 │ │ Agent Transfers │ 2 │ │ Success │ ✅ │ └──────────────────┴────────┘
🔧 Raw Data: {'message': {'messages': [HumanMessage(content='show me the schema of the db', additional_kwargs={}, response_metadata={}, id='678293c5-98b2-4e10-8c1e-a4e500c4ac9b'), AIMessage(content='', additional_...
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[Query #2] 💬 Enter your request: show me the highest 3 stocks by average monthly GDP growth
🔄 ID #session_20250730_165349
🔄 Processing: 'show me the highest 3 stocks by average monthly GDP growth' 💬 Context: 2 previous messages ⏳ Working... [supervisor_2] INFO: Input validation completed successfully. [interpreter] INFO: Input validation passed for Interpreter Agent. [data_prep] INFO: Input validation completed successfully.
Tool info: sql_db_query: Input to this tool is a detailed and correct SQL query, output is a result from the database. If the query is not correct, an error message will be returned. If an error is returned, rewrite the query, check the query, and try again. If you encounter an issue with Unknown column 'xxxx' in 'field list', use sql_db_schema to query the correct table fields. sql_db_schema: Input to this tool is a comma-separated list of tables, output is the schema and sample rows for those tables. Be sure that the tables actually exist by calling sql_db_list_tables first! Example Input: table1, table2, table3 sql_db_list_tables: Input is an empty string, output is a comma-separated list of tables in the database. sql_db_query_checker: Use this tool to double check if your query is correct before executing it. Always use this tool before executing a query with sql_db_query!python_code_sandbox: A secure Python code sandbox. Use this to execute python commands.
- Input should be a valid python command.
- To return output, you should print it out with
print(...). - Don't use f-strings when printing outputs.
- If you need to make web requests, use
httpx.AsyncClient. [data_prep] INFO: Creating workflow for data preparation tasks. [supervisor_2] INFO: Executing data preparation tasks. DEBUG: Found 9 messages: [0] HumanMessage | name: None | has_tool_calls: False | is_tool_msg: False [1] AIMessage | name: None | has_tool_calls: [] | is_tool_msg: False [2] HumanMessage | name: None | has_tool_calls: False | is_tool_msg: False [3] AIMessage | name: Supervisor | has_tool_calls: [{'name': 'transfer_to_data_prep', 'args': {}, 'id': 'call_B3rghU19rTaBEexQDFwp8M7s', 'type': 'tool_call'}] | is_tool_msg: False [4] ToolMessage | name: transfer_to_data_prep | has_tool_calls: False | is_tool_msg: True Tool: transfer_to_data_prep [5] AIMessage | name: data_prep | has_tool_calls: [] | is_tool_msg: False [6] AIMessage | name: data_prep | has_tool_calls: [{'name': 'transfer_back_to_supervisor', 'args': {}, 'id': 'bb772784-9532-4570-8684-2ecc42192b0f', 'type': 'tool_call'}] | is_tool_msg: False [7] ToolMessage | name: transfer_back_to_supervisor | has_tool_calls: False | is_tool_msg: True Tool: transfer_back_to_supervisor [8] AIMessage | name: Supervisor | has_tool_calls: [] | is_tool_msg: False DEBUG: Extracted 2 tool calls ╭───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 📊 SUCCESS ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮ │ 🤖 AI Response: │ │ <thought_process> │ │ The query requires calculating the average monthly GDP growth for each stock and then selecting the top 3 stocks based on this metric. This involves data aggregation and sorting, which is a data preparation task. I delegated this task to the Data Prep agent, which has │ │ provided the results. Now, I will present the top 3 stocks by average monthly GDP growth to the user. │ │ </thought_process> │ │ │ │ │ │ The highest 3 stocks by average monthly GDP growth are: │ │ 1. Dow Jones with an average monthly GDP growth of approximately 2.71% │ │ 2. NASDAQ with an average monthly GDP growth of approximately 2.62% │ │ 3. S&P 500 with an average monthly GDP growth of approximately 2.50% │ │ │ │ │ │ │ │ 🔄 Agent Collaboration: │ │ Supervisor → Data Prep │ ╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
╭─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 🔧 Tool Calls Details ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮ │ │ │ 🔧 Tool Calls │ │ ┏━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ │ │ ┃ Agent ┃ Tool Name ┃ Purpose ┃ Arguments/Query ┃ Result Preview ┃ │ │ ┡━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ │ │ │ Supervisor │ Transfer To Data Prep │ Delegate to Data Prep │ - │ Successfully transferred to data_prep │ │ │ ├────────────┼─────────────────────────────┼───────────────────────┼─────────────────┼──────────────────────────────────────────┤ │ │ │ Data Prep │ Transfer Back To Supervisor │ Return Control │ - │ Successfully transferred back to │ │ │ │ │ │ │ │ Supervisor │ │ │ └────────────┴─────────────────────────────┴───────────────────────┴─────────────────┴──────────────────────────────────────────┘ │ │ │ ╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
🔄 Agent Flow ├── 👤 User: show me the schema of the db ├── 👤 User: show me the highest 3 stocks by average monthly GD... ├── ↔ Transfer To Data Prep ├── 🤖 Data Prep: The highest 3 stocks by average monthly GDP growth are: │ 1. Dow Jones with an average monthly GDP growth of approximately 2.71% │ 2. NASDAQ with an average monthly GDP growth of approximately 2.62% │ 3.... ├── 🤖 Data Prep: Transferring back to Supervisor └── ↔ Transfer Back To Supervisor
📊 Token Usage ┏━━━━━━━━┳━━━━━━━┓ ┃ Type ┃ Count ┃ ┡━━━━━━━━╇━━━━━━━┩ │ Input │ 839 │ │ Output │ 154 │ │ Total │ 993 │ └────────┴───────┘
📈 Execution Statistics ┏━━━━━━━━━━━━━━━━━━┳━━━━━━━┓ ┃ Metric ┃ Value ┃ ┡━━━━━━━━━━━━━━━━━━╇━━━━━━━┩ │ Execution Time │ 9.66s │ │ Agents Involved │ 3 │ │ Total Tool Calls │ 2 │ │ Agent Transfers │ 2 │ │ Success │ ✅ │ └──────────────────┴───────┘
🔧 Raw Data: {'message': {'messages': [HumanMessage(content='show me the schema of the db', additional_kwargs={}, response_metadata={}, id='692a931b-f0ef-470a-82e1-27313a61f9f8'), AIMessage(content='<thought_proce...
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[Query #3] 💬 Enter your request:
- This is what the test run looks like now
- Whatever details we're shoing in Agent Flow section with 🔄 Agent Flow ├── 👤 User: show me the schema of the db ├── ↔ Transfer To Data Prep ├── 🤖 Data Prep: The database contains one table named "finance_data" with the following schema: │ │ - Date (TEXT) │ - Stock Index (TEXT) │ - Open Price (REAL) │ - Close Price (REAL) │ - Daily High (REAL) │ - Daily Low (REAL) │ - Tr... ├── 🤖 Data Prep: Transferring back to Supervisor └── ↔ Transfer Back To Supervisor we need to move the details to a separate pannel called Tool Details.
- The arguments in the Tool calls panel for the Arguments/Query are still empty. Let's try adding tons of print and debug statements throughout to help with the debugging.