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Class info

Classes

Name Children Inherits
ToolContext
llmling_agent.tools.base
Context for tool execution confirmation.
    ToolInfo
    llmling_agent.tools.base
    Information about a registered tool.
      ToolParameter
      llmling_agent.tools.base
      Information about a tool parameter.

        🛈 DocStrings

        Base tool classes.

        ToolContext dataclass

        Context for tool execution confirmation.

        Source code in src/llmling_agent/tools/base.py 
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        @dataclass(frozen=True)
class ToolContext:
    """Context for tool execution confirmation."""

    name: str
    """Name of the tool being executed"""

    args: dict[str, Any]
    """Arguments being passed to the tool"""

    schema: py2openai.OpenAIFunctionTool
    """Complete OpenAI function schema"""

    runtime_ctx: AgentContext
    """Runtime context from agent"""

    @property
    def description(self) -> str | None:
        """Get tool description from schema."""
        return self.schema["function"].get("description")

    @property
    def parameters(self) -> ToolParameters:
        """Get parameter definitions from schema."""
        return self.schema["function"].get("parameters", {})  # type: ignore

    def __str__(self) -> str:
        """Format tool context for logging/display."""
        return (
            f"Tool: {self.name}\n"
            f"Arguments: {self.args}\n"
            f"Description: {self.description or 'N/A'}"
        )

        args instance-attribute 

        args: dict[str, Any]

        Arguments being passed to the tool

        description property 

        description: str | None

        Get tool description from schema.

        name instance-attribute 

        name: str

        Name of the tool being executed

        parameters property 

        parameters: ToolParameters

        Get parameter definitions from schema.

        runtime_ctx instance-attribute 

        runtime_ctx: AgentContext

        Runtime context from agent

        schema instance-attribute 

        schema: OpenAIFunctionTool

        Complete OpenAI function schema

        __str__ 

        __str__() -> str

        Format tool context for logging/display.

        Source code in src/llmling_agent/tools/base.py 
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        def __str__(self) -> str:
    """Format tool context for logging/display."""
    return (
        f"Tool: {self.name}\n"
        f"Arguments: {self.args}\n"
        f"Description: {self.description or 'N/A'}"
    )

        ToolInfo dataclass

        Information about a registered tool.

        Source code in src/llmling_agent/tools/base.py 
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        @dataclass
class ToolInfo:
    """Information about a registered tool."""

    callable: LLMCallableTool
    """The actual tool implementation"""

    enabled: bool = True
    """Whether the tool is currently enabled"""

    source: ToolSource = "runtime"
    """Where the tool came from."""

    priority: int = 100
    """Priority for tool execution (lower = higher priority)"""

    requires_confirmation: bool = False
    """Whether tool execution needs explicit confirmation"""

    requires_capability: str | None = None
    """Optional capability required to use this tool"""

    agent_name: str | None = None
    """The agent name as an identifier for agent-as-a-tool."""

    metadata: dict[str, str] = field(default_factory=dict)
    """Additional tool metadata"""

    cache_enabled: bool = False
    """Whether to enable caching for this tool."""

    @property
    def schema(self) -> py2openai.OpenAIFunctionTool:
        """Get the OpenAI function schema for the tool."""
        return self.callable.get_schema()

    @property
    def name(self) -> str:
        """Get tool name."""
        return self.callable.name

    @property
    def description(self) -> str | None:
        """Get tool description."""
        return self.callable.description

    def matches_filter(self, state: Literal["all", "enabled", "disabled"]) -> bool:
        """Check if tool matches state filter."""
        match state:
            case "all":
                return True
            case "enabled":
                return self.enabled
            case "disabled":
                return not self.enabled

    @property
    def parameters(self) -> list[ToolParameter]:
        """Get information about tool parameters."""
        schema = self.schema["function"]
        properties: dict[str, Property] = schema.get("properties", {})  # type: ignore
        required: list[str] = schema.get("required", [])  # type: ignore

        return [
            ToolParameter(
                name=name,
                required=name in required,
                type_info=details.get("type"),
                description=details.get("description"),
            )
            for name, details in properties.items()
        ]

    def format_info(self, indent: str = "  ") -> str:
        """Format complete tool information."""
        lines = [f"{indent}→ {self.name}"]
        if self.description:
            lines.append(f"{indent}  {self.description}")
        if self.parameters:
            lines.append(f"{indent}  Parameters:")
            lines.extend(f"{indent}    {param}" for param in self.parameters)
        if self.metadata:
            lines.append(f"{indent}  Metadata:")
            lines.extend(f"{indent}    {k}: {v}" for k, v in self.metadata.items())
        return "\n".join(lines)

    async def execute(self, *args: Any, **kwargs: Any) -> Any:
        """Execute tool, handling both sync and async cases."""
        fn = track_tool(self.name)(self.callable.callable)
        return await execute(fn, *args, **kwargs, use_thread=True)

    @classmethod
    def from_code(
        cls,
        code: str,
        name: str | None = None,
        description: str | None = None,
    ) -> Self:
        """Create a tool from a code string."""
        namespace: dict[str, Any] = {}
        exec(code, namespace)
        func = next((v for v in namespace.values() if callable(v)), None)
        if not func:
            msg = "No callable found in provided code"
            raise ValueError(msg)
        return cls.from_callable(
            func, name_override=name, description_override=description
        )

    @classmethod
    def from_callable(
        cls,
        fn: Callable[..., Any] | str,
        *,
        name_override: str | None = None,
        description_override: str | None = None,
        schema_override: py2openai.OpenAIFunctionDefinition | None = None,
        **kwargs: Any,
    ) -> Self:
        tool = LLMCallableTool.from_callable(
            fn,
            name_override=name_override,
            description_override=description_override,
            schema_override=schema_override,
        )
        return cls(tool, **kwargs)

    @classmethod
    def from_crewai_tool(
        cls,
        tool: Any,
        *,
        name_override: str | None = None,
        description_override: str | None = None,
        schema_override: py2openai.OpenAIFunctionDefinition | None = None,
        **kwargs: Any,
    ) -> Self:
        """Allows importing crewai tools."""
        # vaidate_import("crewai_tools", "crewai")
        try:
            from crewai.tools import BaseTool as CrewAiBaseTool
        except ImportError as e:
            msg = "crewai package not found. Please install it with 'pip install crewai'"
            raise ImportError(msg) from e

        if not isinstance(tool, CrewAiBaseTool):
            msg = f"Expected CrewAI BaseTool, got {type(tool)}"
            raise TypeError(msg)

        return cls.from_callable(
            tool._run,
            name_override=name_override or tool.__class__.__name__.removesuffix("Tool"),
            description_override=description_override or tool.description,
            schema_override=schema_override,
            **kwargs,
        )

    @classmethod
    def from_langchain_tool(
        cls,
        tool: Any,
        *,
        name_override: str | None = None,
        description_override: str | None = None,
        schema_override: py2openai.OpenAIFunctionDefinition | None = None,
        **kwargs: Any,
    ) -> Self:
        """Create a tool from a LangChain tool."""
        # vaidate_import("langchain_core", "langchain")
        try:
            from langchain_core.tools import BaseTool as LangChainBaseTool
        except ImportError as e:
            msg = "langchain-core package not found."
            raise ImportError(msg) from e

        if not isinstance(tool, LangChainBaseTool):
            msg = f"Expected LangChain BaseTool, got {type(tool)}"
            raise TypeError(msg)

        return cls.from_callable(
            tool.invoke,
            name_override=name_override or tool.name,
            description_override=description_override or tool.description,
            schema_override=schema_override,
            **kwargs,
        )

    @classmethod
    def from_autogen_tool(
        cls,
        tool: Any,
        *,
        name_override: str | None = None,
        description_override: str | None = None,
        schema_override: py2openai.OpenAIFunctionDefinition | None = None,
        **kwargs: Any,
    ) -> Self:
        """Create a tool from a AutoGen tool."""
        # vaidate_import("autogen_core", "autogen")
        try:
            from autogen_core import CancellationToken
            from autogen_core.tools import BaseTool
        except ImportError as e:
            msg = "autogent_core package not found."
            raise ImportError(msg) from e

        if not isinstance(tool, BaseTool):
            msg = f"Expected AutoGent BaseTool, got {type(tool)}"
            raise TypeError(msg)
        token = CancellationToken()

        input_model = tool.__class__.__orig_bases__[0].__args__[0]  # type: ignore

        name = name_override or tool.name or tool.__class__.__name__.removesuffix("Tool")
        description = (
            description_override
            or tool.description
            or inspect.getdoc(tool.__class__)
            or ""
        )

        async def wrapper(**kwargs: Any) -> Any:
            # Convert kwargs to the expected input model
            model = input_model(**kwargs)
            return await tool.run(model, cancellation_token=token)

        return cls.from_callable(
            wrapper,  # type: ignore
            name_override=name,
            description_override=description,
            schema_override=schema_override,
            **kwargs,
        )

        agent_name class-attribute instance-attribute 

        agent_name: str | None = None

        The agent name as an identifier for agent-as-a-tool.

        cache_enabled class-attribute instance-attribute 

        cache_enabled: bool = False

        Whether to enable caching for this tool.

        callable instance-attribute 

        callable: LLMCallableTool

        The actual tool implementation

        description property 

        description: str | None

        Get tool description.

        enabled class-attribute instance-attribute 

        enabled: bool = True

        Whether the tool is currently enabled

        metadata class-attribute instance-attribute 

        metadata: dict[str, str] = field(default_factory=dict)

        Additional tool metadata

        name property 

        name: str

        Get tool name.

        parameters property 

        parameters: list[ToolParameter]

        Get information about tool parameters.

        priority class-attribute instance-attribute 

        priority: int = 100

        Priority for tool execution (lower = higher priority)

        requires_capability class-attribute instance-attribute 

        requires_capability: str | None = None

        Optional capability required to use this tool

        requires_confirmation class-attribute instance-attribute 

        requires_confirmation: bool = False

        Whether tool execution needs explicit confirmation

        schema property 

        schema: OpenAIFunctionTool

        Get the OpenAI function schema for the tool.

        source class-attribute instance-attribute 

        source: ToolSource = 'runtime'

        Where the tool came from.

        execute async 

        execute(*args: Any, **kwargs: Any) -> Any

        Execute tool, handling both sync and async cases.

        Source code in src/llmling_agent/tools/base.py 
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        async def execute(self, *args: Any, **kwargs: Any) -> Any:
    """Execute tool, handling both sync and async cases."""
    fn = track_tool(self.name)(self.callable.callable)
    return await execute(fn, *args, **kwargs, use_thread=True)

        format_info 

        format_info(indent: str = '  ') -> str

        Format complete tool information.

        Source code in src/llmling_agent/tools/base.py 
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        def format_info(self, indent: str = "  ") -> str:
    """Format complete tool information."""
    lines = [f"{indent}→ {self.name}"]
    if self.description:
        lines.append(f"{indent}  {self.description}")
    if self.parameters:
        lines.append(f"{indent}  Parameters:")
        lines.extend(f"{indent}    {param}" for param in self.parameters)
    if self.metadata:
        lines.append(f"{indent}  Metadata:")
        lines.extend(f"{indent}    {k}: {v}" for k, v in self.metadata.items())
    return "\n".join(lines)

        from_autogen_tool classmethod 

        from_autogen_tool(
    tool: Any,
    *,
    name_override: str | None = None,
    description_override: str | None = None,
    schema_override: OpenAIFunctionDefinition | None = None,
    **kwargs: Any,
) -> Self

        Create a tool from a AutoGen tool.

        Source code in src/llmling_agent/tools/base.py 
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        @classmethod
def from_autogen_tool(
    cls,
    tool: Any,
    *,
    name_override: str | None = None,
    description_override: str | None = None,
    schema_override: py2openai.OpenAIFunctionDefinition | None = None,
    **kwargs: Any,
) -> Self:
    """Create a tool from a AutoGen tool."""
    # vaidate_import("autogen_core", "autogen")
    try:
        from autogen_core import CancellationToken
        from autogen_core.tools import BaseTool
    except ImportError as e:
        msg = "autogent_core package not found."
        raise ImportError(msg) from e

    if not isinstance(tool, BaseTool):
        msg = f"Expected AutoGent BaseTool, got {type(tool)}"
        raise TypeError(msg)
    token = CancellationToken()

    input_model = tool.__class__.__orig_bases__[0].__args__[0]  # type: ignore

    name = name_override or tool.name or tool.__class__.__name__.removesuffix("Tool")
    description = (
        description_override
        or tool.description
        or inspect.getdoc(tool.__class__)
        or ""
    )

    async def wrapper(**kwargs: Any) -> Any:
        # Convert kwargs to the expected input model
        model = input_model(**kwargs)
        return await tool.run(model, cancellation_token=token)

    return cls.from_callable(
        wrapper,  # type: ignore
        name_override=name,
        description_override=description,
        schema_override=schema_override,
        **kwargs,
    )

        from_code classmethod 

        from_code(code: str, name: str | None = None, description: str | None = None) -> Self

        Create a tool from a code string.

        Source code in src/llmling_agent/tools/base.py 
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        @classmethod
def from_code(
    cls,
    code: str,
    name: str | None = None,
    description: str | None = None,
) -> Self:
    """Create a tool from a code string."""
    namespace: dict[str, Any] = {}
    exec(code, namespace)
    func = next((v for v in namespace.values() if callable(v)), None)
    if not func:
        msg = "No callable found in provided code"
        raise ValueError(msg)
    return cls.from_callable(
        func, name_override=name, description_override=description
    )

        from_crewai_tool classmethod 

        from_crewai_tool(
    tool: Any,
    *,
    name_override: str | None = None,
    description_override: str | None = None,
    schema_override: OpenAIFunctionDefinition | None = None,
    **kwargs: Any,
) -> Self

        Allows importing crewai tools.

        Source code in src/llmling_agent/tools/base.py 
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        @classmethod
def from_crewai_tool(
    cls,
    tool: Any,
    *,
    name_override: str | None = None,
    description_override: str | None = None,
    schema_override: py2openai.OpenAIFunctionDefinition | None = None,
    **kwargs: Any,
) -> Self:
    """Allows importing crewai tools."""
    # vaidate_import("crewai_tools", "crewai")
    try:
        from crewai.tools import BaseTool as CrewAiBaseTool
    except ImportError as e:
        msg = "crewai package not found. Please install it with 'pip install crewai'"
        raise ImportError(msg) from e

    if not isinstance(tool, CrewAiBaseTool):
        msg = f"Expected CrewAI BaseTool, got {type(tool)}"
        raise TypeError(msg)

    return cls.from_callable(
        tool._run,
        name_override=name_override or tool.__class__.__name__.removesuffix("Tool"),
        description_override=description_override or tool.description,
        schema_override=schema_override,
        **kwargs,
    )

        from_langchain_tool classmethod 

        from_langchain_tool(
    tool: Any,
    *,
    name_override: str | None = None,
    description_override: str | None = None,
    schema_override: OpenAIFunctionDefinition | None = None,
    **kwargs: Any,
) -> Self

        Create a tool from a LangChain tool.

        Source code in src/llmling_agent/tools/base.py 
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        @classmethod
def from_langchain_tool(
    cls,
    tool: Any,
    *,
    name_override: str | None = None,
    description_override: str | None = None,
    schema_override: py2openai.OpenAIFunctionDefinition | None = None,
    **kwargs: Any,
) -> Self:
    """Create a tool from a LangChain tool."""
    # vaidate_import("langchain_core", "langchain")
    try:
        from langchain_core.tools import BaseTool as LangChainBaseTool
    except ImportError as e:
        msg = "langchain-core package not found."
        raise ImportError(msg) from e

    if not isinstance(tool, LangChainBaseTool):
        msg = f"Expected LangChain BaseTool, got {type(tool)}"
        raise TypeError(msg)

    return cls.from_callable(
        tool.invoke,
        name_override=name_override or tool.name,
        description_override=description_override or tool.description,
        schema_override=schema_override,
        **kwargs,
    )

        matches_filter 

        matches_filter(state: Literal['all', 'enabled', 'disabled']) -> bool

        Check if tool matches state filter.

        Source code in src/llmling_agent/tools/base.py 
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        def matches_filter(self, state: Literal["all", "enabled", "disabled"]) -> bool:
    """Check if tool matches state filter."""
    match state:
        case "all":
            return True
        case "enabled":
            return self.enabled
        case "disabled":
            return not self.enabled

        ToolParameter dataclass

        Information about a tool parameter.

        Source code in src/llmling_agent/tools/base.py 
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        @dataclass
class ToolParameter:
    """Information about a tool parameter."""

    name: str
    required: bool
    type_info: str | None = None
    description: str | None = None

    def __str__(self) -> str:
        """Format parameter info."""
        req = "*" if self.required else ""
        type_str = f": {self.type_info}" if self.type_info else ""
        desc = f" - {self.description}" if self.description else ""
        return f"{self.name}{req}{type_str}{desc}"

        __str__ 

        __str__() -> str

        Format parameter info.

        Source code in src/llmling_agent/tools/base.py 
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        def __str__(self) -> str:
    """Format parameter info."""
    req = "*" if self.required else ""
    type_str = f": {self.type_info}" if self.type_info else ""
    desc = f" - {self.description}" if self.description else ""
    return f"{self.name}{req}{type_str}{desc}"