# llm\_chat\_with\_tool\_call\_output

#### `llm_chat_with_tool_call_output`: Chat with Tool Integration

Allows the language model to decide which tools to use based on the conversation context.

```python
def llm_chat_with_tool_call_output(
    agent_name: str, 
    messages: List[Dict[str, Any]], 
    tools: List[Dict[str, Any]],
    base_url: str = aios_kernel_url,
    llms: List[Dict[str, Any]] = None
) -> LLMResponse
```

**Parameters:**

* `agent_name`: Identifier for the agent making the request
* `messages`: List of message dictionaries
* `tools`: List of available tools and their specifications
* `base_url`: API endpoint URL
* `llms`: Optional list of LLM configurations

**Returns:**

* `LLMResponse` object containing tool calls made by the model

**Example:**

```python
# Chat that may trigger tool use when needed
response = llm_chat_with_tool_call_output(
    "research_assistant",
    messages=[
        {"role": "system", "content": "Help the user with research tasks."},
        {"role": "user", "content": "I need to find recent papers about transformer architectures."}
    ],
    tools=[{
        "name": "scholar_search",
        "description": "Search for academic papers on a topic",
        "parameters": {
            "type": "object",
            "properties": {
                "query": {"type": "string"},
                "year_start": {"type": "integer"},
                "max_results": {"type": "integer"}
            },
            "required": ["query"]
        }
    }]
)

# Check if tool calls were made
tool_calls = json.loads(response["response"]["tool_calls"])
if tool_calls:
    for tool_call in tool_calls:
        print(f"Tool: {tool_call['name']}")
        print(f"Parameters: {tool_call['parameters']}")
```