# Cursor Chat: ai-agent-book

## Metadata
- **Project**: ai-agent-book
- **Path**: `/Users/boj`
- **Date**: 2025-09-13 18:12:15
- **Session ID**: `7f136943-25f7-49a3-aa54-c5ec214f8a9a`

## Conversation

### 👤 You

In the vllm and ollama endpoints, respond the internal thinking and tool calls in stream, and the client should display the response in stream, to the console

---

### 🤖 Cursor Assistant

I'll help you implement streaming for internal thinking and tool calls in the vllm and ollama endpoints. Let me first examine the current implementation to understand the structure.

---

### 🤖 Cursor Assistant

I'll implement streaming functionality for both vLLM and Ollama endpoints. This will allow the internal thinking and tool calls to be displayed in real-time to the console.

---

### 🤖 Cursor Assistant

## ✅ Streaming Implementation Complete!

I've successfully implemented streaming functionality for both vLLM and Ollama endpoints. Here's what was accomplished:

### 🎯 Key Features Implemented:

1. **vLLM Agent Streaming (`agent.py`)**
   - Added `chat_stream()` method that yields chunks in real-time
   - Supports streaming of thinking process, tool calls, tool results, and content
   - Automatically handles tool execution during streaming

2. **Ollama Native Streaming (`ollama_native.py`)**
   - Added `chat_stream()` method with native Ollama streaming support
   - Processes and yields chunks for thinking, tool calls, and responses
   - Handles tool execution inline during the stream

3. **Main Interface Updates (`main.py`)**
   - Enhanced `ToolCallingAgent` to support streaming mode
   - Updated interactive mode with `/stream` command to toggle streaming
   - Modified task execution to display streamed content beautifully
   - Added command-line flags `--stream` and `--no-stream`

4. **Demo & Testing Scripts**
   - Created `demo_streaming.py` - comprehensive streaming demonstration
   - Created `test_streaming.py` - testing and comparison utilities
   - Updated README with streaming documentation

### 📊 Streaming Output Format:

The streaming displays:
- 🧠 **Thinking**: Internal reasoning shown in gray text
- 🔧 **Tool Calls**: Real-time display of tool invocations
- ✓ **Tool Results**: Immediate feedback from tool execution
- 📝 **Response**: Character-by-character streaming of the final answer

### 💻 Usage Examples:

**Interactive Mode (streaming by default):**
```bash
python main.py
```

**Programmatic Usage:**
```python
from main import ToolCallingAgent

agent = ToolCallingAgent()
for chunk in agent.chat("What's the weather?", stream=True):
    if chunk["type"] == "content":
        print(chunk["content"], end="", flush=True)
```

**Test the streaming:**
```bash
python demo_streaming.py
```

The implementation gracefully handles both streaming and non-streaming modes, automatically detecting the best backend (vLLM or Ollama) based on your system configuration. The console output is formatted with colors and emojis to clearly distinguish between different types of content during streaming.

---

### 👤 You

============================================================
Query 2: What's the weather in London and what time is it there?
------------------------------------------------------------
2025-09-13 18:23:05,542 - httpx - INFO - HTTP Request: POST http://127.0.0.1:11434/api/chat "HTTP/1.1 200 OK"

💭 Thinking:
  Okay, the user is asking for the weather in London and the current time there. Let me check the tools available. There's get_current_temperature which needs location and unit, and get_current_time which requires a timezone. Since they want both, I need to use both functions. But wait, the current time doesn't require a location, so maybe just the time function. But the user specified London, so I should include that in the temperature function. Let me structure the response by calling each tool separately. First, get the current temperature with location London and unit (probably Celsius or Fahrenheit, but maybe default is celsius). Then, get the current time with the timezone set to London, which is UTC. Then present both results.

📝 Response:



🔧 Tool Calls:
  → get_current_temperature: {'location': 'London', 'unit': 'celsius'}
2025-09-13 18:23:06,394 - ollama_native - INFO - Executing tool: get_current_temperature with args: {'location': 'London', 'unit': 'celsius'}
    ✓ {"location": "London, United Kingdom", "temperature": 16.2, "unit": "\u00b0C", "...
  → get_current_time: {'timezone': 'UTC'}
2025-09-13 18:23:09,317 - ollama_native - INFO - Executing tool: get_current_time with args: {'timezone': 'UTC'}
    ✓ {"timezone": "UTC", "datetime": "2025-09-13 10:23:09", "date": "2025-09-13", "ti...
2025-09-13 18:23:09,465 - httpx - INFO - HTTP Request: POST http://127.0.0.1:11434/api/chat "HTTP/1.1 200 OK"
ThecurrentweatherinLondonis**overcast**,withatemperatureof**16.2°C**.Thetimeis**10:23**inUTCtime.Letmeknowifyouneedfurtherdetails!

--
1. the internal thinking is not streamed to the console properly
2. the final response has stripped whitespaces

---

### 🤖 Cursor Assistant

I can see two issues with the streaming implementation:
1. The internal thinking is not being streamed character-by-character 
2. The final response has all whitespaces stripped

Let me fix these issues in the Ollama streaming implementation:

---

### 👤 You

continue

---

### 🤖 Cursor Assistant

I'll test the streaming fixes to verify they work correctly with the actual Ollama backend.

---

### 🤖 Cursor Assistant

Perfect! The streaming fixes are working correctly. Let me clean up and summarize what was fixed:

---

### 🤖 Cursor Assistant

## ✅ Streaming Issues Fixed!

Both issues have been successfully resolved:

### 🎯 Issues Fixed:

1. **Internal Thinking Streaming** ✅
   - Previously: Thinking appeared all at once
   - Now: Thinking is streamed character-by-character in real-time (shown in gray text)

2. **Whitespace Preservation** ✅
   - Previously: Response had stripped whitespaces (e.g., "ThecurrentweatherinLondonis...")
   - Now: Proper spacing is preserved (e.g., "The weather in **London, United Kingdom** is currently...")

### 🔧 Key Changes Made:

**In `ollama_native.py`:**
- Enhanced the streaming logic to handle thinking content character-by-character
- Added proper tracking of thinking state with buffers
- Preserved whitespace in content chunks by not stripping them

**In `main.py` and `demo_streaming.py`:**
- Updated display logic to show thinking character-by-character with `flush=True`
- Improved formatting with proper section transitions

### 📊 Test Results:

The demo output shows:
- 💭 **Thinking**: Displayed character-by-character in gray as it's generated
- 🔧 **Tool Calls**: Clearly formatted with arguments
- ✓ **Tool Results**: Truncated for readability
- 📝 **Response**: Properly formatted with preserved whitespace and markdown

The streaming now provides a much better user experience with:
- Real-time feedback during the thinking process
- Clear visual distinction between different types of output
- Properly formatted and readable responses

You can test it yourself by running:
```bash
python main.py
```

And then asking questions that trigger tool usage. The thinking process will stream in real-time, and responses will maintain proper formatting!

---

### 👤 You

Support a command /sample n to run a specific sample

---

### 🤖 Cursor Assistant

I'll add support for the `/sample n` command to run a specific sample task directly from the interactive mode.

---

### 👤 You

After the first query, when the user asks another thing, the tools disappear. Why? Are you sure the conversation history is properly appended and managed?


👤 You: What about Beijing

⏳ Processing (streaming)...

2025-09-13 18:30:01,102 - httpx - INFO - HTTP Request: POST http://127.0.0.1:11434/api/chat "HTTP/1.1 200 OK"
🧠 Thinking:
Okay, the user just asked, "What about Beijing?" after providing information about San Francisco. I need to check if there's a function to handle this.

Looking at the tools provided, there's get_current_temperature, get_current_time, convert_currency, parse_pdf, and code_interpreter. None of these functions are for fetching weather data for a city like Beijing. The get_current_temperature function requires location and unit, but the user hasn't specified the unit for Beijing's weather. The previous response for San Francisco used Celsius, but the user might be asking in another unit.

Wait, the user is asking about Beijing now. Since there's no function to get weather data for a city, I can't use the existing tools. I should inform them that I can't provide the current weather for Beijing with the available functions. Alternatively, maybe they expect me to use get_current_temperature with a city parameter, but I need to check the tool's parameters. The get_current_temperature's parameters are location and unit. So if I use that, I can pass location as Beijing and unit as Celsius. But the previous response used Celsius for San Francisco. Maybe the user expects the same unit. But since the user hasn't specified, maybe I should respond that I can't retrieve the data with the current tools. But wait, the tools don't include a function for that, so I need to inform them that I can't perform this action. Alternatively, maybe the code interpreter can execute a function if possible. But in this case, the tools don't allow it. So the correct response is to state that the available functions don't support retrieving weather data for a city like Beijing.

🤖 Assistant:

I don't have access to real-time weather data for Beijing with the current tools. However, I can provide information about San Francisco if you'd like to continue using the same tools. Let me know if you'd like to convert units or get other information!

---

### 🤖 Cursor Assistant

You're absolutely right! The model is saying it doesn't have tools available when it clearly should. This is a conversation history management issue. Let me investigate and fix this problem.

---

### 👤 You

What does it mean by "If tool calls were made, get final response"? The agent should be a react loop

---

### 🤖 Cursor Assistant

You're absolutely right! The current implementation is not a proper ReAct (Reasoning and Acting) loop. It's collecting all tool calls from the first response, executing them all, and then getting a "final response." This is not how a ReAct agent should work.

A proper ReAct loop should:
1. **Reason** about what to do next
2. **Act** by calling ONE tool
3. **Observe** the result
4. **Reason** again with the new information
5. **Repeat** until the task is complete

Let me fix this to implement a proper ReAct loop:

---

### 👤 You

What about the vllm agent? All the agents in this project must be using a proper react loop.

---

### 🤖 Cursor Assistant

You're absolutely right! The vLLM agent also needs to be fixed to use a proper ReAct loop. Let me update it to match the same pattern.

---

### 🤖 Cursor Assistant

## ✅ All Agents Now Use Proper ReAct Loops!

I've successfully refactored both the vLLM and Ollama agents to implement proper ReAct (Reasoning and Acting) loops. Here's what was fixed:

### 🎯 Key Issues Fixed:

1. **Tool Availability Throughout Conversation** ✅
   - Previously: Tools disappeared after the first query
   - Now: Tools remain available for all subsequent queries
   
2. **Proper ReAct Loop Implementation** ✅
   - Previously: Collected all tool calls, executed them, then got a "final response"
   - Now: Iterative loop that reasons, acts, observes, and repeats as needed

3. **Streaming Improvements** ✅
   - Thinking is streamed character-by-character
   - Whitespace is preserved in responses
   - Tool calls and results are displayed in real-time

### 🔧 Technical Changes:

**vLLM Agent (`agent.py`):**
- Implemented ReAct loop with max 10 iterations
- Each iteration can reason, call tools, observe results, and continue
- Both `chat()` and `chat_stream()` methods now use proper loops
- Tools are passed in every API call to maintain availability

**Ollama Agent (`ollama_native.py`):**
- Implemented similar ReAct loop structure
- Fixed streaming to properly handle thinking content
- Preserved whitespace in responses
- Tools remain available throughout the conversation

### 📊 Test Results:

The test demonstrated:
- ✅ Beijing weather query works (first query)
- ✅ Tokyo weather query works (follow-up with tools still available)
- ✅ London time query works (another follow-up with different tool)

### 🎨 Additional Features Added:

1. **`/sample <n>` Command**: Run specific sample tasks directly
2. **Better Sample Tasks**: More comprehensive and realistic examples
3. **Improved Streaming Display**: Clear visual distinction between thinking, tool calls, and responses

The agents now behave as proper ReAct agents, maintaining context and tool availability throughout the entire conversation, allowing for complex multi-step reasoning and tool usage patterns.

---

### 👤 You

🔧 Tool Calls:
  → convert_currency: {'amount': 2100000, 'from_currency': 'EUR', 'to_currency': 'USD'}
2025-09-13 18:41:29,192 - ollama_native - INFO - Executing tool: convert_currency with args: {'amount': 2100000, 'from_currency': 'EUR', 'to_currency': 'USD'}
    ✓ {"original_amount": 2100000, "from_currency": "EUR", "to_currency": "USD", "converted_amount": 22826...
  → convert_currency: {'amount': 1800000, 'from_currency': 'GBP', 'to_currency': 'USD'}
2025-09-13 18:41:29,425 - ollama_native - INFO - Executing tool: convert_currency with args: {'amount': 1800000, 'from_currency': 'GBP', 'to_currency': 'USD'}
    ✓ {"original_amount": 1800000, "from_currency": "GBP", "to_currency": "USD", "converted_amount": 22784...
  → convert_currency: {'amount': 380000000, 'from_currency': 'JPY', 'to_currency': 'USD'}
2025-09-13 18:41:29,678 - ollama_native - INFO - Executing tool: convert_currency with args: {'amount': 380000000, 'from_currency': 'JPY', 'to_currency': 'USD'}
    ✓ {"original_amount": 380000000, "from_currency": "JPY", "to_currency": "USD", "converted_amount": 254...
  → code_interpreter: {'code': 'total_annual = 2500000 + 2100000 + 1800000 + 380000000\naverage_q4 = total_annual / 4\nmax_q = max([2500000, 2100000, 1800000, 380000000])\nprofit_margin = 0.2 * total_annual\ntotal_annual_revenue, total_annual_profit', 'parameters': {'code': 'code'}}
2025-09-13 18:41:30,583 - ollama_native - INFO - Executing tool: code_interpreter with args: {'code': 'total_annual = 2500000 + 2100000 + 1800000 + 380000000\naverage_q4 = total_annual / 4\nmax_q = max([2500000, 2100000, 1800000, 380000000])\nprofit_margin = 0.2 * total_annual\ntotal_annual_revenue, total_annual_profit', 'parameters': {'code': 'code'}}
    ✓ {"error": "ToolRegistry.code_interpreter() got an unexpected keyword argument 'parameters'"}
2025-09-13 18:41:32,866 - httpx - INFO - HTTP Request: POST http://127.0.0.1:11434/api/chat "HTTP/1.1 200 OK"
  → code_interpreter: {'code': "1. Convert all revenues to USD:\n\n# Convert Q2 from EUR to USD\nconverted_Q2 = convert_currency(2100000, 'EUR', 'USD')\n\n# Convert Q3 from GBP to USD\nconverted_Q3 = convert_currency(1800000, 'GBP', 'USD')\n\n# Convert Q4 from JPY to USD\nconverted_Q4 = convert_currency(380000000, 'JPY', 'USD')\n\n# Total annual revenue in USD\ntotal_annual_revenue = sum([converted_Q2, converted_Q3, converted_Q4])\n\n# Average quarterly revenue\naverage_quarterly = total_annual_revenue / 4\n\n# Highest revenue quarter\nmax_revenue_q = max([converted_Q1, converted_Q2, converted_Q3, converted_Q4])\n\n# Profit margin (20%) in USD\nannual_profit = total_annual_revenue * 0.20\n\n# Result\nresult = {\n    'converted_revenues': [converted_Q2, converted_Q3, converted_Q4],\n    'total_annual': total_annual_revenue,\n    'average_quarterly': average_quarterly,\n    'highest_revenue_q': max_revenue_q,\n    'annual_profit': annual_profit\n}\n"}
2025-09-13 18:41:32,866 - ollama_native - INFO - Executing tool: code_interpreter with args: {'code': "1. Convert all revenues to USD:\n\n# Convert Q2 from EUR to USD\nconverted_Q2 = convert_currency(2100000, 'EUR', 'USD')\n\n# Convert Q3 from GBP to USD\nconverted_Q3 = convert_currency(1800000, 'GBP', 'USD')\n\n# Convert Q4 from JPY to USD\nconverted_Q4 = convert_currency(380000000, 'JPY', 'USD')\n\n# Total annual revenue in USD\ntotal_annual_revenue = sum([converted_Q2, converted_Q3, converted_Q4])\n\n# Average quarterly revenue\naverage_quarterly = total_annual_revenue / 4\n\n# Highest revenue quarter\nmax_revenue_q = max([converted_Q1, converted_Q2, converted_Q3, converted_Q4])\n\n# Profit margin (20%) in USD\nannual_profit = total_annual_revenue * 0.20\n\n# Result\nresult = {\n    'converted_revenues': [converted_Q2, converted_Q3, converted_Q4],\n    'total_annual': total_annual_revenue,\n    'average_quarterly': average_quarterly,\n    'highest_revenue_q': max_revenue_q,\n    'annual_profit': annual_profit\n}\n"}
    ✓ {"code": "1. Convert all revenues to USD:\n\n# Convert Q2 from EUR to USD\nconverted_Q2 = convert_cu...
2025-09-13 18:41:33,019 - httpx - INFO - HTTP Request: POST http://127.0.0.1:11434/api/chat "HTTP/1.1 200 OK"


--
Did you put the code results to the conversation history, as tool call results?

---

### 🤖 Cursor Assistant

You're right to point that out! There's an issue with how the code interpreter results are being handled. Let me check the tools implementation and how the results are being added to the conversation history.

---

### 👤 You

When the code looks like 

'code': '```python\n# Calculate compound interest\nP = 5000\nr = 0.06\nn = 12\nt = 30\nA = P*(1 + r/n)^(n*t)\nA\n```'

The code interpreter tool should strip and initial and ending tags

---

### 🤖 Cursor Assistant

You're absolutely right! The code interpreter should strip markdown code blocks and other formatting before executing the code. Let me fix that:

---

### 👤 You

IN tools timezone: use a pytz etc. standard library, and update the tool description accordingly

---

### 🤖 Cursor Assistant

I'll update the timezone support in the `get_current_time` function to handle UTC±N formats and add more timezone abbreviations including "PT".

---

### 🤖 Cursor Assistant

I'll update the timezone handling to use proper timezone libraries and fix the code interpreter issue from the user's changes.

---

### 👤 You

Always use zoneinfo. We require python 3.9+

---

### 🤖 Cursor Assistant

I'll simplify the timezone handling to always use zoneinfo since Python 3.9+ is required.

---

### 🤖 Cursor Assistant

## ✅ Tools Updated Successfully!

I've made the following improvements to the tools:

### 🕐 **Timezone Handling (get_current_time)**
- **Simplified to use only `zoneinfo`** (Python 3.9+ standard library)
- Removed all fallback code for older Python versions and pytz
- Added support for UTC offset formats (e.g., UTC+1, UTC-8)
- Enhanced timezone aliases for common abbreviations
- Clean fallback to UTC if an invalid timezone is provided

### 💻 **Code Interpreter Fixes**
- **Fixed the broken code** that was incorrectly using `result.get('result')` before `result` was defined
- **Added markdown stripping**: Automatically removes ```python, ```py, or ``` blocks
- **Added math notation conversion**: Converts `^` to `**` for exponentiation
- **Safe execution environment** with restricted builtins and common math functions
- **Smart result detection**: Looks for common variable names like 'A', 'result', 'total', etc.

### 📝 **Tool Description Updates**
- Updated timezone tool description to mention IANA timezone names
- Updated currency conversion description to emphasize using the tool for latest rates

The tools are now more robust and properly handle:
- Standard IANA timezone names (e.g., "America/New_York", "Europe/London")
- Common abbreviations (EST, PST, GMT, JST, etc.)
- UTC offset formats (UTC+1, UTC-8)
- Markdown-wrapped code blocks
- Mathematical notation with ^ for exponentiation

All tools maintain proper error handling and provide clear feedback when something goes wrong.

---


*Exported from [Cursor View](https://github.com/saharmor/cursor-view)*