Why Is My LangChain Tool Calling Not Producing a Final AI Response?

Below is some guidance based on how LangChain’s tool calling is designed to work.

How the Process Is Designed

According to the tutorial, the tool calling process happens in two steps:

1. Initial Invocation:
   Your first call to llm.invoke() sends the human message. The model then “thinks” about how to answer the query and, instead of returning a complete answer, it produces an AI message with empty content and a set of tool calls (e.g. for “multiply” and “add”). This is expected behavior when the model decides that a tool is needed.

2. Tool Execution & Follow-up Invocation:
   You then extract the tool calls, execute them (using your function implementations), and append the resulting ToolMessages (which contain the results) to your conversation history. The expectation is that a subsequent call to llm.invoke() (the “follow‐up” round) will have enough context (human message, previous tool call instruction, and the tool results) so that the model can generate a final answer that summarizes or contextualizes the tool outputs.

What May Be Happening for You

In your output the final llm.invoke() returns an AI message with nearly empty content (only 1 token and finish_reason eos). There are a few possibilities:

• Model Support:
  The model you are using (mistralai/Mixtral-8x7B-Instruct-v0.1) may not fully support the two-stage function calling flow (i.e. generating a proper final answer after receiving tool responses). The LangChain documentation examples typically reference models that are known to work well with function calling (for example ChatGPT or ChatEdenAI in the provided examples). You might try using a model that is known to support this flow.

• Instruction and Prompting:
  The expected behavior of generating a natural-language answer that references the tool results (e.g. “The result of 3 times 12 is 36...”) depends on the model correctly inferring the user intent once it sees the tool responses. Some models require additional instructions or prompt engineering to encourage a synthetic final answer rather than simply terminating. In your case, the conversation history does include the human query and the tool messages, but the model might be treating the conversation as complete or might not be “prompted” to produce a natural language message.

• Version Compatibility:
  Also, make sure you are using a langchain-core version that supports automatic conversion of tool call outputs into ToolMessages (>= 0.2.19) as the documentation indicates.

Next Steps

Try the following suggestions:

1. Test with a Different Model:
   Use a model known to support function calling (for example, one of the OpenAI Chat models or ChatEdenAI as in the documentation example) to verify that the two-stage process produces the expected natural language output.

2. Adjust Prompting:
   You might explicitly instruct the model in the human prompt to “explain” the result after the tool calls are complete. For example, appending an extra instruction like “Now explain the result of the calculations” could help.

3. Verify Version:
   Ensure that your LangChain packages (especially langchain-core) are up to date.

Example Final Flow

Here’s a simplified version of the code flow from the documentation:

PYTHONCopied
from langchain_core.messages import HumanMessage, ToolMessage
from langchain_core.tools import tool
from langchain_openai import ChatOpenAI

# Set up LLM that supports tool calling
llm = ChatOpenAI(
    base_url="https://api.together.xyz/v1",
    api_key="<api_key>",
    model="mistralai/Mixtral-8x7B-Instruct-v0.1",
)

@tool
def add(a: int, b: int) -> int:
    return a + b

@tool
def multiply(a: int, b: int) -> int:
    return a * b

tools = [add, multiply]
llm = llm.bind_tools(tools)

# Round 1: Ask the question
messages = [HumanMessage("What is 3 * 12? Also, what is 11 + 49?")]
response = llm.invoke(messages)

# Print the initial tool calls (this is expected behavior)
print(response)
print("-----------------------------------")

if response.tool_calls:
    # Append the first round dialogue
    messages.append(response)
    
    # Process tool calls and append their results
    for tool_call in response.tool_calls:
        selected_tool = {"add": add, "multiply": multiply}[tool_call["name"].lower()]
        tool_msg = selected_tool.invoke(tool_call)
        messages.append(tool_msg)

    print(messages)
    print("-----------------------------------")

    # Round 2: Provide conversation history with tool outputs to generate final answer
    second_response = llm.invoke(messages)
    print(second_response)

In this flow the final answer (in second_response.content) should ideally include a natural language answer embedding the tool outputs. If it doesn’t, it is most likely due to either model limitations or missing prompt instructions.

Conclusion

Your code is following the documented pattern, so if you’re getting an empty final response it is likely that the model you are using isn’t producing the expected final elaboration. Consider switching to a model that is known to support this two-step tool calling flow or adjusting your prompts to encourage a fuller response.