Why the ChatAgent drop the original response message content of model and replace it with FunctionCallingMessage with an empty content

[farmer00317558]

        assist_msg = FunctionCallingMessage(
            role_name=self.role_name,
            role_type=self.role_type,
            meta_dict=None,
            content="",
            func_name=func_name,
            args=args,
            tool_call_id=tool_call_id,
        )

This is a snippet from the _record_tool_calling method. Why is the content property not set to the actual output of the current step model?

[KeepALifeUS]

This is a common issue with function calling message handling. Here's what's happening and how to work around it:

The Problem

# Model returns:
{
    "role": "assistant",
    "content": "Let me search for that...",  # Original content
    "tool_calls": [{"function": {...}}]
}

# After processing, you get:
FunctionCallingMessage(
    content="",  # Empty!
    func_name="search",
    args={...}
)

The original content is dropped because CAMEL treats tool calls as discrete operations.

Why This Happens

In the function calling paradigm:

1. Content before tool call = "thinking out loud" (not actionable)
2. Tool call = the actual action
3. Frameworks often prioritize the action over the commentary

Workarounds

1. Capture Pre-Call Content

class ContentPreservingAgent(ChatAgent):
    def step(self, input_message):
        response = super().step(input_message)
        
        # Access raw response before transformation
        if hasattr(response, "_raw_response"):
            original_content = response._raw_response.get("content", "")
            if original_content:
                response.metadata["original_content"] = original_content
        
        return response

2. Use Two-Phase Execution

# Phase 1: Get reasoning
reasoning = agent.step("Think about: " + query)

# Phase 2: Execute with tools
action = agent.step("Now execute: " + query, allow_tools=True)

# Combine
full_response = {
    "reasoning": reasoning.content,
    "action": action.func_name,
    "result": action.result
}

3. Request Explicit Reasoning

system_prompt = """
Before calling any tool, always:
1. State your reasoning in <thinking> tags
2. Then make the tool call

Example:
<thinking>User wants X, I'll use tool Y because...</thinking>
[tool call]
"""

Recommended Approach

Store reasoning in external state rather than relying on message content:

state = {
    "reasoning_trace": [],
    "actions": []
}

# Agent writes reasoning to state
state["reasoning_trace"].append(agent.last_reasoning)

More on state-based patterns: https://github.com/KeepALifeUS/autonomous-agents