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XML-based Tools

Available in Langroid since v0.17.0.

XMLToolMessage is an abstract class for tools formatted using XML instead of JSON. It has been mainly tested with non-nested tool structures.

For example in test_xml_tool_message.py we define a CodeTool as follows (slightly simplified here):

class CodeTool(XMLToolMessage):
    request: str = "code_tool"
    purpose: str = "Tool for writing <code> to a <filepath>"

    filepath: str = Field(
        ..., 
        description="The path to the file to write the code to"
    )

    code: str = Field(
        ..., 
        description="The code to write to the file", 
        verbatim=True
    )

Especially note how the code field has verbatim=True set in the Field metadata. This will ensure that the LLM receives instructions to

  • enclose code field contents in a CDATA section, and
  • leave the code contents intact, without any escaping or other modifications.

Contrast this with a JSON-based tool, where newlines, quotes, etc need to be escaped. LLMs (especially weaker ones) often "forget" to do the right escaping, which leads to incorrect JSON, and creates a burden on us to "repair" the resulting json, a fraught process at best. Moreover, studies have shown that requiring that an LLM return this type of carefully escaped code within a JSON string can lead to a significant drop in the quality of the code generated1.

Note that tools/functions in OpenAI and related APIs are exclusively JSON-based, so in langroid when enabling an agent to use a tool derived from XMLToolMessage, we set these flags in ChatAgentConfig:

  • use_functions_api=False (disables OpenAI functions/tools)
  • use_tools=True (enables Langroid-native prompt-based tools)

See also the WriteFileTool for a concrete example of a tool derived from XMLToolMessage. This tool enables an LLM to write content (code or text) to a file.

If you are using an existing Langroid ToolMessage, e.g. SendTool, you can define your own subclass of SendTool, say XMLSendTool, inheriting from both SendTool and XMLToolMessage; see this example

  1. LLMs are bad at returning code in JSON. ↩