Langchain: Resolving ToolRuntime Errors With Args_schema

Hey everyone, let's dive into a common snag you might hit when using Langchain, specifically when you're trying to marry the @tool(args_schema=...) decorator with ToolRuntime. If you're anything like me, you've probably run into a situation where, despite your best efforts, Langchain throws a TypeError: get_weather() missing 1 required positional argument: 'runtime' error. Don't worry, we're going to break down why this happens and how to fix it, so you can get back to building awesome Langchain applications without the headache. Let's get started, shall we?

The Core Issue: Combining args_schema and ToolRuntime

So, what's the deal? You've got your tool all set up, probably something like this:

from langchain.tools import tool
from pydantic import BaseModel, Field
from langchain.tools.tool_manager import ToolRuntime

class WeatherInput(BaseModel):
    """Input for weather queries."""
    location: str = Field(description="City name or coordinates")

@tool(args_schema=WeatherInput)
def get_weather(location: str, runtime: ToolRuntime) -> str:
    """Get current weather"""
    # ... your weather fetching logic here ...

Looks pretty good, right? You've defined your input schema using pydantic and specified that your get_weather function takes a location (a string) and a runtime: ToolRuntime. You're using the @tool decorator to make it all nice and tidy for Langchain to understand. But when you run this code, especially when it's integrated into a create_agent setup, you get that pesky TypeError. The error message is screaming at you that you're missing the runtime argument. Why is this happening? Let's get to the bottom of it.

The heart of the problem lies in how Langchain handles the arguments when you use both args_schema and ToolRuntime. The @tool decorator, when combined with args_schema, expects to pass the parsed arguments from the schema to your function. However, it doesn't automatically know how to provide the ToolRuntime instance. This is where the confusion begins, and why the TypeError pops up. Essentially, you're telling Langchain, "Hey, I've got this tool, it takes this input, and oh yeah, I also need this runtime thing." But Langchain isn't quite sure how to give you that runtime. We'll explore the solutions in the next sections, so hold tight.

Now, before we jump into the solutions, it's worth understanding why you'd even want to use ToolRuntime in the first place. This is crucial because it helps us to better understand the correct approaches. The ToolRuntime object gives you important context when your tool is running – things like the tool's execution ID, any metadata associated with the tool's run, and access to other useful information that can help you with logging, debugging, or managing your tools more effectively. So, if you are looking to get detailed insights from your Langchain setup, then you definitely want to utilize ToolRuntime.

Solution 1: Adjusting the Function Signature

Okay, so the most straightforward solution involves adjusting the way you define your function's arguments. You don't necessarily need to pass runtime directly into your function if you can access the runtime context through other means. Here's how you might refactor your code:

from langchain.tools import tool
from pydantic import BaseModel, Field
from langchain.tools.tool_manager import ToolRuntime
from langchain.callbacks.base import BaseCallbackManager

class WeatherInput(BaseModel):
    """Input for weather queries."""
    location: str = Field(description="City name or coordinates")

@tool(args_schema=WeatherInput)
def get_weather(location: str, callback_manager: BaseCallbackManager) -> str:
    """Get current weather"""
    # Access the runtime through the callback_manager
    # Example: You may have to adjust based on your langchain version and how you're using it.
    # runtime = callback_manager.get_child_run("tool_run_id") # This is an example, adjust accordingly
    print(f"Fetching weather for: {location}")
    # ... your weather fetching logic here ...
    return "Sunny in {location}"

In this adjusted example, instead of explicitly requesting runtime, we're making use of the callback system. The exact approach to getting the runtime information depends on how your Langchain application is set up and how you're using the callbacks. But the underlying principle remains the same: Leverage the context provided by Langchain through its execution environment. While this example provides a generic solution, there may be instances where it may not apply.

The beauty of this approach is that it reduces the likelihood of directly encountering the TypeError. Furthermore, it can make your code cleaner because you're less likely to need to worry about managing the runtime object directly in your tool function's signature. This approach works by having Langchain manage the underlying runtime context and making it accessible through more generalized mechanisms, such as callback managers.

Solution 2: Modifying the Agent's Tool Invocation (If Possible)

Another approach involves tweaking how your agent calls the tool. This is more of a workaround, and it might not always be feasible, depending on how your agent is set up. The idea is to make sure you're passing the runtime argument correctly when you instantiate the agent or call your tool. This is dependent on your specific agent setup and the version of Langchain you are using.

Here’s a general idea (though specifics will vary):

from langchain.agents import create_react_agent, AgentExecutor
from langchain.tools import tool
from pydantic import BaseModel, Field
from langchain.tools.tool_manager import ToolRuntime
from langchain.llms import OpenAI

class WeatherInput(BaseModel):
    """Input for weather queries."""
    location: str = Field(description="City name or coordinates")

@tool(args_schema=WeatherInput)
def get_weather(location: str, runtime: ToolRuntime) -> str:
    """Get current weather"""
    print(f"Fetching weather for: {location}")
    return "Sunny in {location}"

# Assuming you have an llm and tools defined
llm = OpenAI(temperature=0)
tools = [get_weather]
agent = create_react_agent(llm=llm, tools=tools)
agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)

# When calling the tool, try to ensure the runtime is passed.  This may involve modifying your AgentExecutor or tool setup.
# Example: (This part is illustrative; your implementation may differ significantly.)
# agent_executor.run("What's the weather in London?", tool_runtime=ToolRuntime(...))

The most important thing here is to verify how your agent passes arguments to the tool. You might have to step through your agent's code to understand how arguments are being handled and modify the call accordingly. This solution usually involves checking the initialization or execution logic of your agent to identify the specific part where the tool is called and then adding logic to supply the required runtime object. Note that depending on the version of Langchain, the exact method for injecting ToolRuntime will vary, thus, you may have to refer to the official Langchain documentation for more precise guidelines. Always test your solution thoroughly to ensure the runtime is being passed correctly.

Solution 3: Checking Langchain Version and Documentation

Langchain is constantly evolving, so the way tools and runtimes are handled might change between versions. So, this brings us to a really important point: check your Langchain version and refer to the official documentation. You might find that the issue has been addressed in a newer version, or the recommended way of using ToolRuntime has evolved.

• Upgrade if Possible: If you're on an older version, consider upgrading to the latest stable release. Often, the latest versions include bug fixes and improvements that could resolve the TypeError and provide more elegant ways to handle ToolRuntime.
• Documentation is Your Friend: The Langchain documentation is comprehensive. Consult the specific sections on tools, agents, and callbacks. These sections will offer insights into how to integrate ToolRuntime correctly, how to access it, and any caveats to be aware of.
• Example Code: Pay attention to the examples provided in the documentation. They will show you the correct syntax and best practices for your specific use case. The documentation often includes complete, working code samples that you can adapt for your needs.

Debugging Tips

If you're still stuck, here are some debugging tips:

• Print Statements: Use print() statements liberally. Print the arguments passed to your tool function to see exactly what's being sent. This can help you identify if the runtime object is even present or if it's being passed in an unexpected way.
• Inspect the Agent: If you're using an agent, examine its initialization and the steps it takes to call your tools. Print out the tool call details (name, arguments) to understand how the agent is interacting with your tools.
• Simplify: Try creating a minimal, reproducible example. This involves stripping down your code to the bare essentials (the tool definition, a simple agent setup) to isolate the problem. This can help you focus on the root cause without getting distracted by other parts of your application.
• Breakpoints: Use a debugger to step through your code line by line. This will allow you to inspect the values of variables and understand the flow of execution, helping you pinpoint where the TypeError originates.

Conclusion

Dealing with the TypeError: get_weather() missing 1 required positional argument: 'runtime' can be frustrating, but by understanding the interplay between @tool(args_schema=...) and ToolRuntime, you can effectively solve this problem. Whether you choose to adjust your function signature, modify your agent's tool invocation, or consult the latest Langchain documentation, you have several options to get your tools running smoothly. Remember to check your Langchain version, refer to the documentation, and, most importantly, don't be afraid to experiment and debug! Happy coding, and may your Langchain adventures be bug-free!