> ## Documentation Index
> Fetch the complete documentation index at: https://docs.camel-ai.org/llms.txt
> Use this file to discover all available pages before exploring further.
# Camel.agents. utils
## build\_default\_summary\_prompt
```python theme={"system"}
def build_default_summary_prompt(conversation_text: str):
```
Create the default prompt used for conversation summarization.
**Parameters:**
* **conversation\_text** (str): The conversation to be summarized.
**Returns:**
str: A formatted prompt instructing the model to produce a structured
markdown summary.
## generate\_tool\_prompt
```python theme={"system"}
def generate_tool_prompt(tool_schema_list: List[Dict[str, Any]]):
```
**Returns:**
str: A string representing the tool prompt.
## extract\_tool\_call
```python theme={"system"}
def extract_tool_call(content: str):
```
Extract the tool call from the model response, if present.
**Parameters:**
* **response** (Any): The model's response object.
**Returns:**
Optional\[Dict\[str, Any]]: The parsed tool call if present,
otherwise None.
## safe\_model\_dump
```python theme={"system"}
def safe_model_dump(obj):
```
Safely dump a Pydantic model to a dictionary.
This method attempts to use the `model_dump` method if available,
otherwise it falls back to the `dict` method.
## convert\_to\_function\_tool
```python theme={"system"}
def convert_to_function_tool(tool: Union[FunctionTool, Callable]):
```
Convert a tool to a FunctionTool from Callable.
## convert\_to\_schema
```python theme={"system"}
def convert_to_schema(tool: Union[FunctionTool, Callable, Dict[str, Any]]):
```
Convert a tool to a schema from Callable or FunctionTool.
## get\_info\_dict
```python theme={"system"}
def get_info_dict(
session_id: Optional[str],
usage: Optional[Dict[str, int]],
termination_reasons: List[str],
num_tokens: int,
tool_calls: List[ToolCallingRecord],
external_tool_call_requests: Optional[List[ToolCallRequest]] = None
):
```
Returns a dictionary containing information about the chat session.
**Parameters:**
* **session\_id** (str, optional): The ID of the chat session.
* **usage** (Dict\[str, int], optional): Information about the usage of the LLM.
* **termination\_reasons** (List\[str]): The reasons for the termination of the chat session.
* **num\_tokens** (int): The number of tokens used in the chat session.
* **tool\_calls** (List\[ToolCallingRecord]): The list of function calling records, containing the information of called tools.
* **external\_tool\_call\_requests** (Optional\[List\[ToolCallRequest]]): The requests for external tool calls.
**Returns:**
Dict\[str, Any]: The chat session information.
## handle\_logprobs
```python theme={"system"}
def handle_logprobs(choice: Choice):
```