how many tools can i use in langchain agent? if i have 2000 token context length than i can add much more tools like thousands of tools. because all have do read by llm which will be out of context length . am i right?

[dahwin]

if i have 2000 token context length than i can add much more tools like thousands of tools. because all have do read by llm which will be out of context length . am i right?

[dosubot[bot]]

🤖

You're correct in your understanding that the number of tools you can add to the LangChain agent is limited by the context length of the language model. Each tool's name, description, and arguments are converted into a string and included in the prompt that is fed to the language model. If the total number of tokens in the prompt exceeds the context length of the language model, it will not be able to process the prompt correctly.

This is evident from the create_prompt method in the StructuredChatAgent class. Here's a snippet of the code:

@classmethod
def create_prompt(
    cls,
    tools: Sequence[BaseTool],
    prefix: str = PREFIX,
    suffix: str = SUFFIX,
    human_message_template: str = HUMAN_MESSAGE_TEMPLATE,
    format_instructions: str = FORMAT_INSTRUCTIONS,
    input_variables: Optional[List[str]] = None,
    memory_prompts: Optional[List[BasePromptTemplate]] = None,
) -> BasePromptTemplate:
    tool_strings = []
    for tool in tools:
        args_schema = re.sub("}", "}}}}", re.sub("{", "{{{{", str(tool.args)))
        tool_strings.append(f"{tool.name}: {tool.description}, args: {args_schema}")
    formatted_tools = "\n".join(tool_strings)
    ...
    template = "\n\n".join([prefix, formatted_tools, format_instructions, suffix])
    ...

In this method, each tool's name, description, and arguments are converted into a string and added to the tool_strings list. These strings are then joined together with newline characters to form formatted_tools, which is included in the final prompt template. If the total number of tokens in formatted_tools and the rest of the prompt exceeds the language model's context length, the language model will not be able to process the prompt.

The maximum context length that the language model used in LangChain can handle is determined by the max_tokens parameter. As per the provided code, the default value of max_tokens is set to 256. However, if max_tokens is set to -1, it returns as many tokens as possible given the prompt and the model's maximal context size.

Please note that the actual maximum context length might be limited by the specific language model used. For example, GPT-3 has a maximum token limit of 2048 tokens.

In the context shared, it's shown that LangChain has a method modelname_to_contextsize that maps model names to their respective maximum token limits. For instance, "gpt-4" has a maximum token limit of 8192, while "text-davinci-003" has a limit of 4097. This method is used to determine the maximum context size for the model in use. Therefore, the maximum context length can also be influenced by the specific model chosen for the task.

In conclusion, while it might be technically possible to add thousands of tools to the LangChain agent, the practical limit will be determined by the context length of the language model. If you try to add too many tools such that the total number of tokens in the prompt exceeds the language model's context length, the language model will not be able to process the prompt correctly.

Sources

• libs/langchain/langchain/agents/structured_chat/base.py
• libs/langchain/langchain/llms/openai.py
• libs/langchain/langchain/chat_models/openai.py

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