liqiang
b119135836
12 KiB
Cursor Chat: ai-agent-book
Metadata
- Project: ai-agent-book
- Path:
/Users/boj - Date: 2025-09-11 19:03:30
- Session ID:
c41b929b-a694-4e46-a7fe-ce756b7b97d6
Conversation
👤 You
How is - full
- no_history
- no_reasoning
- no_tool_calls
- no_tool_results
modes implemented in context project
🤖 Cursor Assistant
I'll help you understand how those modes are implemented in the context project. Let me search for the relevant code.
🤖 Cursor Assistant
The context modes in the context project are implemented through a systematic ablation framework designed to test the importance of different contextual components in agent performance. memory:8629901 memory:8629921
The implementation centers around the ContextMode enumeration defined in /Users/boj/ai-agent-book/projects/week1/context/agent.py, which establishes five distinct operational modes. The FULL mode serves as the baseline with complete functionality, while the other four modes systematically remove specific capabilities to measure their impact on agent performance. memory:8629917
The NO_HISTORY mode prevents the agent from accessing previous tool call history within conversation iterations. When this mode is active, the _build_context method excludes the tool call history section entirely, and the context update mechanism in line 631 of the agent file bypasses injecting historical context into the system message. This forces the agent to operate without knowledge of what tools it has previously executed.
The NO_REASONING mode eliminates the reasoning steps tracking from the context. The implementation checks for this mode at line 440 in the _build_context method and skips adding the "Previous Reasoning Steps" section when active. This removes the agent's ability to reference its previous thought processes during multi-step problem solving.
The NO_TOOL_CALLS mode represents the most restrictive modification, completely disabling the agent's ability to use tools. Lines 559-570 in the generate_response method conditionally exclude tool descriptions and set tool_choice parameters to None when this mode is active. The agent must attempt to answer questions without accessing any external tools like PDF parsing or web search capabilities.
The NO_TOOL_RESULTS mode allows tool execution but hides the results from subsequent iterations. Lines 612-624 implement this by replacing actual tool results with a placeholder message "[Tool result hidden due to context mode]" in the conversation history. The agent can still call tools but cannot see what those tools returned, testing whether awareness of tool outputs affects decision-making.
Each mode integrates into the AgentTrajectory dataclass which tracks execution history with the active context mode. The trajectory maintains reasoning steps and tool calls throughout the conversation, but the visibility and usage of this information depends on the active mode. The agent constructor accepts the context mode as a parameter, propagating it through all relevant methods that build context or handle tool interactions.
The ablation test suite in main.py orchestrates experiments across all modes, measuring metrics like success rate, iteration count, tool call frequency, and execution time. This systematic approach quantifies how each contextual component contributes to overall agent effectiveness in complex tasks requiring multiple reasoning steps and tool interactions.
👤 You
================================================================================ 📤 ITERATION 2 - FULL REQUEST JSON:
{ "model": "doubao-seed-1-6-thinking-250715", "messages": [ { "role": "system", "content": "You are an intelligent assistant with access to tools. \n \n\n\n## Tool Call History:\n- Called convert_currency with args: {"amount": 2100000, "from_currency": "EUR", "to_currency": "USD"}\n Result: {\n "original_amount": 2100000,\n "from_currency": "EUR",\n "to_currency": "USD",\n "converted_amount": 2282608.7,\n "exchange_rate": 1.087,\n "timestamp": "2025-09-11T19:01:41.962330"\n}\n- Called convert_currency with args: {"amount": 1800000, "from_currency": "GBP", "to_currency": "USD"}\n Result: {\n "original_amount": 1800000,\n "from_currency": "GBP",\n "to_currency": "USD",\n "converted_amount": 2278481.01,\n "exchange_rate": 1.2658,\n "timestamp": "2025-09-11T19:01:41.962654"\n}\n- Called convert_currency with args: {"amount": 380000000, "from_currency": "JPY", "to_currency": "USD"}\n Result: {\n "original_amount": 380000000,\n "from_currency": "JPY",\n "to_currency": "USD",\n "converted_amount": 2541806.02,\n "exchange_rate": 0.0067,\n "timestamp": "2025-09-11T19:01:41.963160"\n}\n\n\nYour task is to solve the given problem using the available tools. Think step by step and use tools as needed.\n\nImportant: When you have gathered all necessary information and computed the final answer, clearly state "FINAL ANSWER:" followed by your answer." }, { "role": "user", "content": "A company has the following quarterly revenues:\n- Q1: $2,500,000 USD\n- Q2: €2,100,000 EUR\n- Q3: £1,800,000 GBP\n- Q4: ¥380,000,000 JPY\n\nPlease:\n1. Convert all revenues to USD\n2. Calculate the total annual revenue in USD\n3. Determine the average quarterly revenue\n4. Find which quarter had the highest revenue\n5. If the company has a 20% profit margin, calculate the annual profit in USD" }, { "content": "", "refusal": null, "role": "assistant", "annotations": null, "audio": null, "function_call": null, "tool_calls": [ { "id": "call_7t8tme51dxflsyvblgxhgtax", "function": { "arguments": "{"amount":2100000,"from_currency":"EUR","to_currency":"USD"}", "name": "convert_currency" }, "type": "function" }, { "id": "call_11ypxblsdthj0wmgy556n9gl", "function": { "arguments": "{"amount":1800000,"from_currency":"GBP","to_currency":"USD"}", "name": "convert_currency" }, "type": "function" }, { "id": "call_p7ojsq4btup8d37j2zoi3grg", "function": { "arguments": "{"amount":380000000,"from_currency":"JPY","to_currency":"USD"}", "name": "convert_currency" }, "type": "function" } ], "reasoning_content": "Got it, let's tackle this problem step by step. First, I need to convert all the non-USD revenues to USD. For that, I need the current exchange rates for EUR to USD, GBP to USD, and JPY to USD. But wait, the convert_currency tool requires the amount, from_currency, and to_currency. So I need to call convert_currency for each of Q2, Q3, Q4.\n\nFirst, Q2 is €2,100,000 EUR to USD. So call convert_currency with amount=2100000, from_currency=EUR, to_currency=USD.\n\nQ3 is £1,800,000 GBP to USD. Call convert_currency with amount=1800000, from_currency=GBP, to_currency=USD.\n\nQ4 is ¥380,000,000 JPY to USD. Call convert_currency with amount=380000000, from_currency=JPY, to_currency=USD.\n\nQ1 is already in USD, so no conversion needed.\n\nOnce I have all four quarters in USD, I can sum them for total annual revenue. Then divide by 4 for average quarterly. Then compare each quarter's USD value to find the highest. Then take 20% of the total for annual profit.\n\nBut wait, the convert_currency tool gives the converted amount. So I need to get those values first. Since I can do parallel tool calls, I can call all three convert_currency functions at once. Let's list the calls:\n\n1. Q2 conversion: {"name":"convert_currency","parameters":{"amount":2100000,"from_currency":"EUR","to_currency":"USD"},"id":0}\n2. Q3 conversion: {"name":"convert_currency","parameters":{"amount":1800000,"from_currency":"GBP","to_currency":"USD"},"id":1}\n3. Q4 conversion: {"name":"convert_currency","parameters":{"amount":380000000,"from_currency":"JPY","to_currency":"USD"},"id":2}\n\nYes, that's a parallel call. Once I have those results, I can proceed with the rest. But first, I need to get those converted values. So the first step is to make these three convert_currency calls in parallel." }, { "role": "tool", "tool_call_id": "call_7t8tme51dxflsyvblgxhgtax", "content": "{"original_amount": 2100000, "from_currency": "EUR", "to_currency": "USD", "converted_amount": 2282608.7, "exchange_rate": 1.087, "timestamp": "2025-09-11T19:01:41.962330"}" }, { "role": "tool", "tool_call_id": "call_11ypxblsdthj0wmgy556n9gl", "content": "{"original_amount": 1800000, "from_currency": "GBP", "to_currency": "USD", "converted_amount": 2278481.01, "exchange_rate": 1.2658, "timestamp": "2025-09-11T19:01:41.962654"}" }, { "role": "tool", "tool_call_id": "call_p7ojsq4btup8d37j2zoi3grg", "content": "{"original_amount": 380000000, "from_currency": "JPY", "to_currency": "USD", "converted_amount": 2541806.02, "exchange_rate": 0.0067, "timestamp": "2025-09-11T19:01:41.963160"}" } ],
-- for no reasoning, you need to remove reasoning_content from message history when appending assistant messages
🤖 Cursor Assistant
Looking at the JSON request you've shown, you're absolutely right - there's an issue with how the NO_REASONING mode is implemented. The reasoning_content field is still being included in the assistant messages when they're added to the message history, even when the context mode is set to NO_REASONING.
Let me examine where assistant messages are being appended to the messages list:
🤖 Cursor Assistant
You've identified a critical bug in the NO_REASONING mode implementation. memory:8629917 memory:8629921 The current code adds the complete assistant message to the conversation history including the reasoning_content field, even when the context mode is set to NO_REASONING. This defeats the purpose of the ablation study for that mode.
The issue occurs at three locations in the generate_response method where assistant messages are appended to the messages list (lines 587, 593, and 627). These lines use message.dict() or message.model_dump() which includes all fields from the message object.
To fix this, the code needs to filter out the reasoning_content field when NO_REASONING mode is active. Here's the necessary correction:
🤖 Cursor Assistant
Now let me update the three locations where assistant messages are appended to use this new helper method:
🤖 Cursor Assistant
The fix has been successfully implemented. memory:8629917 memory:8629921 The issue you identified was that the reasoning_content field was being included in the message history even when the NO_REASONING context mode was active, which defeated the purpose of that ablation mode.
The solution adds a new helper method _prepare_assistant_message that filters out the reasoning_content field when the context mode is set to NO_REASONING. This method is now called at all three locations where assistant messages are appended to the conversation history (lines 606, 612, and 646).
With this fix, when the agent operates in NO_REASONING mode, the reasoning content will be properly excluded from the message history, ensuring that subsequent iterations of the conversation don't have access to the agent's internal reasoning process. This preserves the integrity of the ablation study for measuring the impact of reasoning visibility on agent performance.
The implementation maintains backward compatibility with other context modes - the reasoning_content field is only removed when specifically in NO_REASONING mode, leaving all other modes unaffected.
Exported from Cursor View