Understanding langchain's `llm.bind_tools` internal mechanism

Other Questions langchain
1

I’m trying to figure out the internal workings of langchain’s tool binding functionality. I want to know how the framework transforms code-based tool definitions into text prompts that can be understood by language models.

Please correct me if my understanding is wrong anywhere.

Here’s the bind_tools method I’m looking at:

class BaseChatOpenAI(BaseChatModel):
    def bind_tools(
        self,
        tools: Sequence[Union[Dict[str, Any], Type, Callable, BaseTool]],
        **kwargs: Any,
    ) -> Runnable[LanguageModelInput, BaseMessage]:
        """Bind tool-like objects to this chat model.
        Assumes model is compatible with OpenAI tool-calling API.
        """
        return super().bind(tools=formatted_tools, **kwargs)

When I trace the super() call, I find:

class Runnable(Generic[Input, Output], ABC):
    def bind(self, **kwargs: Any) -> Runnable[Input, Output]:
        """Bind arguments to a Runnable, returning a new Runnable."""
        return RunnableBinding(bound=self, kwargs=kwargs, config={})

And looking at RunnableBinding:

class RunnableBinding(RunnableBindingBase[Input, Output]):
    """Wrap a Runnable with additional functionality.
    
    Example usage:
    
    from langchain_community.chat_models import ChatOpenAI
    chat_model = ChatOpenAI()
    chat_model.invoke('Say "Bird-MAGIC"', stop=['-'])
    
    bound_model = chat_model.bind(stop=['-'])
    bound_model.invoke('Say "Bird-MAGIC"')
    """
    
    def bind(self, **kwargs: Any) -> Runnable[Input, Output]:
        return self.__class__(
            bound=self.bound,
            config=self.config,
            kwargs={**self.kwargs, **kwargs},
            custom_input_type=self.custom_input_type,
            custom_output_type=self.custom_output_type,
        )

I’m stuck at this point and can’t see how the tool information actually gets passed to the language model.

2

You’re missing the execution path. RunnableBinding just stores tools in kwargs - the actual conversion happens later when the model runs. When you call invoke(), the model’s _generate method grabs those stored tools and uses langchain’s format_tool_to_openai_function to convert them into JSON schema for OpenAI. It takes your Python function signatures, docstrings, and type hints and turns them into proper OpenAI function definitions with parameters and descriptions. Here’s the key: bind_tools is just prep work - nothing gets sent to the model until you invoke it. Want to see the actual conversion? Check the _generate method in your specific model class, not the binding mechanism. The binding is basically a deferred operation that gets resolved when the model makes its API call.

3

the real fun starts in the invoke or stream methods! when you call them, the kwargs (tools included) are formatted to fit OpenAI’s tool specs and bundled in the API request. it’s like magic, lol!

4

Yup, exactly! bind is just holding onto your stuff for later. Invoke() does the real magic - that’s when langchain takes your functions and converts 'em into JSON schemas via format_tool_to_openai_tool. Check out the _make_request method to see it all come together with the API payload.

5

You stopped tracing right before the good stuff. RunnableBinding just wraps your tools in kwargs - it doesn’t do the actual work.

The real action’s in the chat model’s invoke method. That’s where it grabs those stored kwargs and runs them through _convert_to_tool_dict (or something similar). These functions take your Python code and convert it to OpenAI’s JSON format.

I went down this same rabbit hole last year debugging function calls. What actually helped was dropping print statements into the HTTP client code where it builds the API request. You’ll see exactly what JSON gets sent to OpenAI.

The payload has your original prompt plus a “tools” array with function schemas. Each schema includes the function name, description from your docstrings, and parameter types from your signatures.

OpenAI reads your text prompt and tool definitions, then decides whether to respond with regular text or make tool calls.

Want to see the actual conversion? Trace through the model’s invoke method, not the binding layer. That’s where your tools finally get processed into API format.

6

The actual tool processing happens in the model’s _generate method - you haven’t hit that part in your trace yet. When RunnableBinding calls the underlying model, it passes those stored kwargs (including the formatted tools) to the generation logic. Inside _generate, langchain grabs the tools from kwargs and converts them to OpenAI’s function calling format using convert_to_openai_function utilities. These transform your Python tool definitions into JSON schema objects with parameters, descriptions, and types. The schemas get added to the API payload as the “tools” parameter with your messages. The LLM sees your text prompt plus these tool schemas, so it can choose between regular text or structured tool calls. I found this by setting breakpoints in the HTTP request prep code instead of the binding layer.

7

You’re close but missing the main piece. The tool conversion happens when RunnableBinding.invoke() gets called.

The binding stores those formatted tools in kwargs, but doesn’t process them until execution. During invoke, langchain converts your tool definitions to OpenAI’s function calling format - JSON schema describing your functions.

The LLM gets these tool schemas with your prompt, then responds with structured data showing which tools to call and what parameters to use.

Debugging langchain internals is honestly a pain. I’ve spent way too much time tracing through these abstractions just to figure out what’s happening.

For automation stuff, I just skip the complexity and use Latenode. You can connect LLMs directly to tools and APIs without dealing with binding mechanisms. The visual workflow shows exactly how data flows from prompt to tool execution and back.

Much simpler than digging through source code for what should be basic functionality.