#!/usr/bin/env python
# coding=utf-8
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import annotations
import ast
import inspect
import json
import logging
import os
import sys
import tempfile
import textwrap
import types
import warnings
from collections.abc import Callable
from contextlib import contextmanager
from functools import wraps
from pathlib import Path
from typing import TYPE_CHECKING, Any
from huggingface_hub import (
CommitOperationAdd,
create_commit,
create_repo,
get_collection,
hf_hub_download,
metadata_update,
)
from ._function_type_hints_utils import (
TypeHintParsingException,
_convert_type_hints_to_json_schema,
_get_json_schema_type,
get_imports,
get_json_schema,
)
from .agent_types import handle_agent_input_types, handle_agent_output_types
from .tool_validation import MethodChecker, validate_tool_attributes
from .utils import (
BASE_BUILTIN_MODULES,
_is_package_available,
get_source,
instance_to_source,
is_valid_name,
)
if TYPE_CHECKING:
import mcp
logger = logging.getLogger(__name__)
def validate_after_init(cls):
original_init = cls.__init__
@wraps(original_init)
def new_init(self, *args, **kwargs):
original_init(self, *args, **kwargs)
self.validate_arguments()
cls.__init__ = new_init
return cls
AUTHORIZED_TYPES = [
"string",
"boolean",
"integer",
"number",
"image",
"audio",
"array",
"object",
"any",
"null",
]
CONVERSION_DICT = {"str": "string", "int": "integer", "float": "number"}
class Tool:
"""
A base class for the functions used by the agent. Subclass this and implement the `forward` method as well as the
following class attributes:
- **description** (`str`) -- A short description of what your tool does, the inputs it expects and the output(s) it
will return. For instance 'This is a tool that downloads a file from a `url`. It takes the `url` as input, and
returns the text contained in the file'.
- **name** (`str`) -- A performative name that will be used for your tool in the prompt to the agent. For instance
`"text-classifier"` or `"image_generator"`.
- **inputs** (`Dict[str, Dict[str, Union[str, type, bool]]]`) -- The dict of modalities expected for the inputs.
It has one `type`key and a `description`key.
This is used by `launch_gradio_demo` or to make a nice space from your tool, and also can be used in the generated
description for your tool.
- **output_type** (`type`) -- The type of the tool output. This is used by `launch_gradio_demo`
or to make a nice space from your tool, and also can be used in the generated description for your tool.
You can also override the method [`~Tool.setup`] if your tool has an expensive operation to perform before being
usable (such as loading a model). [`~Tool.setup`] will be called the first time you use your tool, but not at
instantiation.
"""
name: str
description: str
inputs: dict[str, dict[str, str | type | bool]]
output_type: str
def __init__(self, *args, **kwargs):
self.is_initialized = False
def __init_subclass__(cls, **kwargs):
super().__init_subclass__(**kwargs)
validate_after_init(cls)
def validate_arguments(self):
required_attributes = {
"description": str,
"name": str,
"inputs": dict,
"output_type": str,
}
# Validate class attributes
for attr, expected_type in required_attributes.items():
attr_value = getattr(self, attr, None)
if attr_value is None:
raise TypeError(f"You must set an attribute {attr}.")
if not isinstance(attr_value, expected_type):
raise TypeError(
f"Attribute {attr} should have type {expected_type.__name__}, got {type(attr_value)} instead."
)
# - Validate name
if not is_valid_name(self.name):
raise Exception(
f"Invalid Tool name '{self.name}': must be a valid Python identifier and not a reserved keyword"
)
# Validate inputs
for input_name, input_content in self.inputs.items():
assert isinstance(input_content, dict), f"Input '{input_name}' should be a dictionary."
assert "type" in input_content and "description" in input_content, (
f"Input '{input_name}' should have keys 'type' and 'description', has only {list(input_content.keys())}."
)
if input_content["type"] not in AUTHORIZED_TYPES:
raise Exception(
f"Input '{input_name}': type '{input_content['type']}' is not an authorized value, should be one of {AUTHORIZED_TYPES}."
)
# Validate output type
assert getattr(self, "output_type", None) in AUTHORIZED_TYPES
# Validate forward function signature, except for Tools that use a "generic" signature (PipelineTool, SpaceToolWrapper, LangChainToolWrapper)
if not (
hasattr(self, "skip_forward_signature_validation")
and getattr(self, "skip_forward_signature_validation") is True
):
signature = inspect.signature(self.forward)
actual_keys = set(key for key in signature.parameters.keys() if key != "self")
expected_keys = set(self.inputs.keys())
if actual_keys != expected_keys:
raise Exception(
f"In tool '{self.name}', 'forward' method parameters were {actual_keys}, but expected {expected_keys}. "
f"It should take 'self' as its first argument, then its next arguments should match the keys of tool attribute 'inputs'."
)
json_schema = _convert_type_hints_to_json_schema(self.forward, error_on_missing_type_hints=False)[
"properties"
] # This function will not raise an error on missing docstrings, contrary to get_json_schema
for key, value in self.inputs.items():
assert key in json_schema, (
f"Input '{key}' should be present in function signature, found only {json_schema.keys()}"
)
if "nullable" in value:
assert "nullable" in json_schema[key], (
f"Nullable argument '{key}' in inputs should have key 'nullable' set to True in function signature."
)
if key in json_schema and "nullable" in json_schema[key]:
assert "nullable" in value, (
f"Nullable argument '{key}' in function signature should have key 'nullable' set to True in inputs."
)
def forward(self, *args, **kwargs):
return NotImplementedError("Write this method in your subclass of `Tool`.")
def __call__(self, *args, sanitize_inputs_outputs: bool = False, **kwargs):
if not self.is_initialized:
self.setup()
# Handle the arguments might be passed as a single dictionary
if len(args) == 1 and len(kwargs) == 0 and isinstance(args[0], dict):
potential_kwargs = args[0]
# If the dictionary keys match our input parameters, convert it to kwargs
if all(key in self.inputs for key in potential_kwargs):
args = ()
kwargs = potential_kwargs
if sanitize_inputs_outputs:
args, kwargs = handle_agent_input_types(*args, **kwargs)
outputs = self.forward(*args, **kwargs)
if sanitize_inputs_outputs:
outputs = handle_agent_output_types(outputs, self.output_type)
return outputs
def setup(self):
"""
Overwrite this method here for any operation that is expensive and needs to be executed before you start using
your tool. Such as loading a big model.
"""
self.is_initialized = True
def to_dict(self) -> dict:
"""Returns a dictionary representing the tool"""
class_name = self.__class__.__name__
if type(self).__name__ == "SimpleTool":
# Check that imports are self-contained
source_code = get_source(self.forward).replace("@tool", "")
forward_node = ast.parse(source_code)
# If tool was created using '@tool' decorator, it has only a forward pass, so it's simpler to just get its code
method_checker = MethodChecker(set())
method_checker.visit(forward_node)
if len(method_checker.errors) > 0:
errors = [f"- {error}" for error in method_checker.errors]
raise (ValueError(f"SimpleTool validation failed for {self.name}:\n" + "\n".join(errors)))
forward_source_code = get_source(self.forward)
tool_code = textwrap.dedent(
f"""
from smolagents import Tool
from typing import Any, Optional
class {class_name}(Tool):
name = "{self.name}"
description = {json.dumps(textwrap.dedent(self.description).strip())}
inputs = {repr(self.inputs)}
output_type = "{self.output_type}"
"""
).strip()
import re
def add_self_argument(source_code: str) -> str:
"""Add 'self' as first argument to a function definition if not present."""
pattern = r"def forward\(((?!self)[^)]*)\)"
def replacement(match):
args = match.group(1).strip()
if args: # If there are other arguments
return f"def forward(self, {args})"
return "def forward(self)"
return re.sub(pattern, replacement, source_code)
forward_source_code = forward_source_code.replace(self.name, "forward")
forward_source_code = add_self_argument(forward_source_code)
forward_source_code = forward_source_code.replace("@tool", "").strip()
tool_code += "\n\n" + textwrap.indent(forward_source_code, " ")
else: # If the tool was not created by the @tool decorator, it was made by subclassing Tool
if type(self).__name__ in [
"SpaceToolWrapper",
"LangChainToolWrapper",
"GradioToolWrapper",
]:
raise ValueError(
"Cannot save objects created with from_space, from_langchain or from_gradio, as this would create errors."
)
validate_tool_attributes(self.__class__)
tool_code = "from typing import Any, Optional\n" + instance_to_source(self, base_cls=Tool)
requirements = {el for el in get_imports(tool_code) if el not in sys.stdlib_module_names} | {"smolagents"}
return {"name": self.name, "code": tool_code, "requirements": sorted(requirements)}
@classmethod
def from_dict(cls, tool_dict: dict[str, Any], **kwargs) -> "Tool":
"""
Create tool from a dictionary representation.
Args:
tool_dict (`dict[str, Any]`): Dictionary representation of the tool.
**kwargs: Additional keyword arguments to pass to the tool's constructor.
Returns:
`Tool`: Tool object.
"""
if "code" not in tool_dict:
raise ValueError("Tool dictionary must contain 'code' key with the tool source code")
return cls.from_code(tool_dict["code"], **kwargs)
def save(self, output_dir: str | Path, tool_file_name: str = "tool", make_gradio_app: bool = True):
"""
Saves the relevant code files for your tool so it can be pushed to the Hub. This will copy the code of your
tool in `output_dir` as well as autogenerate:
- a `{tool_file_name}.py` file containing the logic for your tool.
If you pass `make_gradio_app=True`, this will also write:
- an `app.py` file providing a UI for your tool when it is exported to a Space with `tool.push_to_hub()`
- a `requirements.txt` containing the names of the modules used by your tool (as detected when inspecting its
code)
Args:
output_dir (`str` or `Path`): The folder in which you want to save your tool.
tool_file_name (`str`, *optional*): The file name in which you want to save your tool.
make_gradio_app (`bool`, *optional*, defaults to True): Whether to also export a `requirements.txt` file and Gradio UI.
"""
# Ensure output directory exists
output_path = Path(output_dir)
output_path.mkdir(parents=True, exist_ok=True)
# Save tool file
self._write_file(output_path / f"{tool_file_name}.py", self._get_tool_code())
if make_gradio_app:
# Save app file
self._write_file(output_path / "app.py", self._get_gradio_app_code(tool_module_name=tool_file_name))
# Save requirements file
self._write_file(output_path / "requirements.txt", self._get_requirements())
def _write_file(self, file_path: Path, content: str) -> None:
"""Writes content to a file with UTF-8 encoding."""
file_path.write_text(content, encoding="utf-8")
def push_to_hub(
self,
repo_id: str,
commit_message: str = "Upload tool",
private: bool | None = None,
token: bool | str | None = None,
create_pr: bool = False,
) -> str:
"""
Upload the tool to the Hub.
Parameters:
repo_id (`str`):
The name of the repository you want to push your tool to. It should contain your organization name when
pushing to a given organization.
commit_message (`str`, *optional*, defaults to `"Upload tool"`):
Message to commit while pushing.
private (`bool`, *optional*):
Whether to make the repo private. If `None` (default), the repo will be public unless the organization's default is private. This value is ignored if the repo already exists.
token (`bool` or `str`, *optional*):
The token to use as HTTP bearer authorization for remote files. If unset, will use the token generated
when running `huggingface-cli login` (stored in `~/.huggingface`).
create_pr (`bool`, *optional*, defaults to `False`):
Whether to create a PR with the uploaded files or directly commit.
"""
# Initialize repository
repo_id = self._initialize_hub_repo(repo_id, token, private)
# Prepare files for commit
additions = self._prepare_hub_files()
# Create commit
return create_commit(
repo_id=repo_id,
operations=additions,
commit_message=commit_message,
token=token,
create_pr=create_pr,
repo_type="space",
)
@staticmethod
def _initialize_hub_repo(repo_id: str, token: bool | str | None, private: bool | None) -> str:
"""Initialize repository on Hugging Face Hub."""
repo_url = create_repo(
repo_id=repo_id,
token=token,
private=private,
exist_ok=True,
repo_type="space",
space_sdk="gradio",
)
metadata_update(repo_url.repo_id, {"tags": ["smolagents", "tool"]}, repo_type="space", token=token)
return repo_url.repo_id
def _prepare_hub_files(self) -> list:
"""Prepare files for Hub commit."""
additions = [
# Add tool code
CommitOperationAdd(
path_in_repo="tool.py",
path_or_fileobj=self._get_tool_code().encode(),
),
# Add Gradio app
CommitOperationAdd(
path_in_repo="app.py",
path_or_fileobj=self._get_gradio_app_code().encode(),
),
# Add requirements
CommitOperationAdd(
path_in_repo="requirements.txt",
path_or_fileobj=self._get_requirements().encode(),
),
]
return additions
def _get_tool_code(self) -> str:
"""Get the tool's code."""
return self.to_dict()["code"]
def _get_gradio_app_code(self, tool_module_name: str = "tool") -> str:
"""Get the Gradio app code."""
class_name = self.__class__.__name__
return textwrap.dedent(
f"""\
from smolagents import launch_gradio_demo
from {tool_module_name} import {class_name}
tool = {class_name}()
launch_gradio_demo(tool)
"""
)
def _get_requirements(self) -> str:
"""Get the requirements."""
return "\n".join(self.to_dict()["requirements"])
@classmethod
def from_hub(
cls,
repo_id: str,
token: str | None = None,
trust_remote_code: bool = False,
**kwargs,
):
"""
Loads a tool defined on the Hub.
Loading a tool from the Hub means that you'll download the tool and execute it locally.
ALWAYS inspect the tool you're downloading before loading it within your runtime, as you would do when
installing a package using pip/npm/apt.
Args:
repo_id (`str`):
The name of the Space repo on the Hub where your tool is defined.
token (`str`, *optional*):
The token to identify you on hf.co. If unset, will use the token generated when running
`huggingface-cli login` (stored in `~/.huggingface`).
trust_remote_code(`str`, *optional*, defaults to False):
This flags marks that you understand the risk of running remote code and that you trust this tool.
If not setting this to True, loading the tool from Hub will fail.
kwargs (additional keyword arguments, *optional*):
Additional keyword arguments that will be split in two: all arguments relevant to the Hub (such as
`cache_dir`, `revision`, `subfolder`) will be used when downloading the files for your tool, and the
others will be passed along to its init.
"""
if not trust_remote_code:
raise ValueError(
"Loading a tool from Hub requires to acknowledge you trust its code: to do so, pass `trust_remote_code=True`."
)
# Get the tool's tool.py file.
tool_file = hf_hub_download(
repo_id,
"tool.py",
token=token,
repo_type="space",
cache_dir=kwargs.get("cache_dir"),
force_download=kwargs.get("force_download"),
proxies=kwargs.get("proxies"),
revision=kwargs.get("revision"),
subfolder=kwargs.get("subfolder"),
local_files_only=kwargs.get("local_files_only"),
)
tool_code = Path(tool_file).read_text()
return Tool.from_code(tool_code, **kwargs)
@classmethod
def from_code(cls, tool_code: str, **kwargs):
module = types.ModuleType("dynamic_tool")
exec(tool_code, module.__dict__)
# Find the Tool subclass
tool_class = next(
(
obj
for _, obj in inspect.getmembers(module, inspect.isclass)
if issubclass(obj, Tool) and obj is not Tool
),
None,
)
if tool_class is None:
raise ValueError("No Tool subclass found in the code.")
if not isinstance(tool_class.inputs, dict):
tool_class.inputs = ast.literal_eval(tool_class.inputs)
return tool_class(**kwargs)
@staticmethod
def from_space(
space_id: str,
name: str,
description: str,
api_name: str | None = None,
token: str | None = None,
):
"""
Creates a [`Tool`] from a Space given its id on the Hub.
Args:
space_id (`str`):
The id of the Space on the Hub.
name (`str`):
The name of the tool.
description (`str`):
The description of the tool.
api_name (`str`, *optional*):
The specific api_name to use, if the space has several tabs. If not precised, will default to the first available api.
token (`str`, *optional*):
Add your token to access private spaces or increase your GPU quotas.
Returns:
[`Tool`]:
The Space, as a tool.
Examples:
```py
>>> image_generator = Tool.from_space(
... space_id="black-forest-labs/FLUX.1-schnell",
... name="image-generator",
... description="Generate an image from a prompt"
... )
>>> image = image_generator("Generate an image of a cool surfer in Tahiti")
```
```py
>>> face_swapper = Tool.from_space(
... "tuan2308/face-swap",
... "face_swapper",
... "Tool that puts the face shown on the first image on the second image. You can give it paths to images.",
... )
>>> image = face_swapper('./aymeric.jpeg', './ruth.jpg')
```
"""
from gradio_client import Client, handle_file
class SpaceToolWrapper(Tool):
skip_forward_signature_validation = True
def __init__(
self,
space_id: str,
name: str,
description: str,
api_name: str | None = None,
token: str | None = None,
):
self.name = name
self.description = description
self.client = Client(space_id, hf_token=token)
space_description = self.client.view_api(return_format="dict", print_info=False)["named_endpoints"]
# If api_name is not defined, take the first of the available APIs for this space
if api_name is None:
api_name = list(space_description.keys())[0]
logger.warning(
f"Since `api_name` was not defined, it was automatically set to the first available API: `{api_name}`."
)
self.api_name = api_name
try:
space_description_api = space_description[api_name]
except KeyError:
raise KeyError(f"Could not find specified {api_name=} among available api names.")
self.inputs = {}
for parameter in space_description_api["parameters"]:
if not parameter["parameter_has_default"]:
parameter_type = parameter["type"]["type"]
if parameter_type == "object":
parameter_type = "any"
self.inputs[parameter["parameter_name"]] = {
"type": parameter_type,
"description": parameter["python_type"]["description"],
}
output_component = space_description_api["returns"][0]["component"]
if output_component == "Image":
self.output_type = "image"
elif output_component == "Audio":
self.output_type = "audio"
else:
self.output_type = "any"
self.is_initialized = True
def sanitize_argument_for_prediction(self, arg):
from gradio_client.utils import is_http_url_like
from PIL.Image import Image
if isinstance(arg, Image):
temp_file = tempfile.NamedTemporaryFile(suffix=".png", delete=False)
arg.save(temp_file.name)
arg = temp_file.name
if (
(isinstance(arg, str) and os.path.isfile(arg))
or (isinstance(arg, Path) and arg.exists() and arg.is_file())
or is_http_url_like(arg)
):
arg = handle_file(arg)
return arg
def forward(self, *args, **kwargs):
# Preprocess args and kwargs:
args = list(args)
for i, arg in enumerate(args):
args[i] = self.sanitize_argument_for_prediction(arg)
for arg_name, arg in kwargs.items():
kwargs[arg_name] = self.sanitize_argument_for_prediction(arg)
output = self.client.predict(*args, api_name=self.api_name, **kwargs)
if isinstance(output, tuple) or isinstance(output, list):
return output[
0
] # Sometime the space also returns the generation seed, in which case the result is at index 0
return output
return SpaceToolWrapper(
space_id=space_id,
name=name,
description=description,
api_name=api_name,
token=token,
)
@staticmethod
def from_gradio(gradio_tool):
"""
Creates a [`Tool`] from a gradio tool.
"""
import inspect
class GradioToolWrapper(Tool):
def __init__(self, _gradio_tool):
self.name = _gradio_tool.name
self.description = _gradio_tool.description
self.output_type = "string"
self._gradio_tool = _gradio_tool
func_args = list(inspect.signature(_gradio_tool.run).parameters.items())
self.inputs = {
key: {"type": CONVERSION_DICT[value.annotation], "description": ""} for key, value in func_args
}
self.forward = self._gradio_tool.run
return GradioToolWrapper(gradio_tool)
@staticmethod
def from_langchain(langchain_tool):
"""
Creates a [`Tool`] from a langchain tool.
"""
class LangChainToolWrapper(Tool):
skip_forward_signature_validation = True
def __init__(self, _langchain_tool):
self.name = _langchain_tool.name.lower()
self.description = _langchain_tool.description
self.inputs = _langchain_tool.args.copy()
for input_content in self.inputs.values():
if "title" in input_content:
input_content.pop("title")
input_content["description"] = ""
self.output_type = "string"
self.langchain_tool = _langchain_tool
self.is_initialized = True
def forward(self, *args, **kwargs):
tool_input = kwargs.copy()
for index, argument in enumerate(args):
if index < len(self.inputs):
input_key = next(iter(self.inputs))
tool_input[input_key] = argument
return self.langchain_tool.run(tool_input)
return LangChainToolWrapper(langchain_tool)
def launch_gradio_demo(tool: Tool):
"""
Launches a gradio demo for a tool. The corresponding tool class needs to properly implement the class attributes
`inputs` and `output_type`.
Args:
tool (`Tool`): The tool for which to launch the demo.
"""
try:
import gradio as gr
except ImportError:
raise ImportError("Gradio should be installed in order to launch a gradio demo.")
TYPE_TO_COMPONENT_CLASS_MAPPING = {
"boolean": gr.Checkbox,
"image": gr.Image,
"audio": gr.Audio,
"string": gr.Textbox,
"integer": gr.Textbox,
"number": gr.Textbox,
}
def tool_forward(*args, **kwargs):
return tool(*args, sanitize_inputs_outputs=True, **kwargs)
tool_forward.__signature__ = inspect.signature(tool.forward)
gradio_inputs = []
for input_name, input_details in tool.inputs.items():
input_gradio_component_class = TYPE_TO_COMPONENT_CLASS_MAPPING[input_details["type"]]
new_component = input_gradio_component_class(label=input_name)
gradio_inputs.append(new_component)
output_gradio_component_class = TYPE_TO_COMPONENT_CLASS_MAPPING[tool.output_type]
gradio_output = output_gradio_component_class(label="Output")
gr.Interface(
fn=tool_forward,
inputs=gradio_inputs,
outputs=gradio_output,
title=tool.name,
description=tool.description,
api_name=tool.name,
).launch()
def load_tool(
repo_id,
model_repo_id: str | None = None,
token: str | None = None,
trust_remote_code: bool = False,
**kwargs,
):
"""
Main function to quickly load a tool from the Hub.
Loading a tool means that you'll download the tool and execute it locally.
ALWAYS inspect the tool you're downloading before loading it within your runtime, as you would do when
installing a package using pip/npm/apt.
Args:
repo_id (`str`):
Space repo ID of a tool on the Hub.
model_repo_id (`str`, *optional*):
Use this argument to use a different model than the default one for the tool you selected.
token (`str`, *optional*):
The token to identify you on hf.co. If unset, will use the token generated when running `huggingface-cli
login` (stored in `~/.huggingface`).
trust_remote_code (`bool`, *optional*, defaults to False):
This needs to be accepted in order to load a tool from Hub.
kwargs (additional keyword arguments, *optional*):
Additional keyword arguments that will be split in two: all arguments relevant to the Hub (such as
`cache_dir`, `revision`, `subfolder`) will be used when downloading the files for your tool, and the others
will be passed along to its init.
"""
return Tool.from_hub(
repo_id,
model_repo_id=model_repo_id,
token=token,
trust_remote_code=trust_remote_code,
**kwargs,
)
def add_description(description):
"""
A decorator that adds a description to a function.
"""
def inner(func):
func.description = description
func.name = func.__name__
return func
return inner
class ToolCollection:
"""
Tool collections enable loading a collection of tools in the agent's toolbox.
Collections can be loaded from a collection in the Hub or from an MCP server, see:
- [`ToolCollection.from_hub`]
- [`ToolCollection.from_mcp`]
For example and usage, see: [`ToolCollection.from_hub`] and [`ToolCollection.from_mcp`]
"""
def __init__(self, tools: list[Tool]):
self.tools = tools
@classmethod
def from_hub(
cls,
collection_slug: str,
token: str | None = None,
trust_remote_code: bool = False,
) -> "ToolCollection":
"""Loads a tool collection from the Hub.
it adds a collection of tools from all Spaces in the collection to the agent's toolbox
> [!NOTE]
> Only Spaces will be fetched, so you can feel free to add models and datasets to your collection if you'd
> like for this collection to showcase them.
Args:
collection_slug (str): The collection slug referencing the collection.
token (str, *optional*): The authentication token if the collection is private.
trust_remote_code (bool, *optional*, defaults to False): Whether to trust the remote code.
Returns:
ToolCollection: A tool collection instance loaded with the tools.
Example:
```py
>>> from smolagents import ToolCollection, CodeAgent
>>> image_tool_collection = ToolCollection.from_hub("huggingface-tools/diffusion-tools-6630bb19a942c2306a2cdb6f")
>>> agent = CodeAgent(tools=[*image_tool_collection.tools], add_base_tools=True)
>>> agent.run("Please draw me a picture of rivers and lakes.")
```
"""
_collection = get_collection(collection_slug, token=token)
_hub_repo_ids = {item.item_id for item in _collection.items if item.item_type == "space"}
tools = {Tool.from_hub(repo_id, token, trust_remote_code) for repo_id in _hub_repo_ids}
return cls(tools)
@classmethod
@contextmanager
def from_mcp(
cls, server_parameters: "mcp.StdioServerParameters" | dict, trust_remote_code: bool = False
) -> "ToolCollection":
"""Automatically load a tool collection from an MCP server.
This method supports Stdio, Streamable HTTP, and legacy HTTP+SSE MCP servers. Look at the `server_parameters`
argument for more details on how to connect to each MCP server.
Note: a separate thread will be spawned to run an asyncio event loop handling
the MCP server.
Args:
server_parameters (`mcp.StdioServerParameters` or `dict`):
Configuration parameters to connect to the MCP server. This can be:
- An instance of `mcp.StdioServerParameters` for connecting a Stdio MCP server via standard input/output using a subprocess.
- A `dict` with at least:
- "url": URL of the server.
- "transport": Transport protocol to use, one of:
- "streamable-http": (recommended) Streamable HTTP transport.
- "sse": Legacy HTTP+SSE transport (deprecated).
If "transport" is omitted, the legacy "sse" transport is assumed (a deprecation warning will be issued).
The HTTP+SSE transport is deprecated and future behavior will default to the Streamable HTTP transport.
Please pass explicitly the "transport" key.
trust_remote_code (`bool`, *optional*, defaults to `False`):
Whether to trust the execution of code from tools defined on the MCP server.
This option should only be set to `True` if you trust the MCP server,
and undertand the risks associated with running remote code on your local machine.
If set to `False`, loading tools from MCP will fail.
Returns:
ToolCollection: A tool collection instance.
Example with a Stdio MCP server:
```py
>>> import os
>>> from smolagents import ToolCollection, CodeAgent, InferenceClientModel
>>> from mcp import StdioServerParameters
>>> model = InferenceClientModel()
>>> server_parameters = StdioServerParameters(
>>> command="uvx",
>>> args=["--quiet", "pubmedmcp@0.1.3"],
>>> env={"UV_PYTHON": "3.12", **os.environ},
>>> )
>>> with ToolCollection.from_mcp(server_parameters, trust_remote_code=True) as tool_collection:
>>> agent = CodeAgent(tools=[*tool_collection.tools], add_base_tools=True, model=model)
>>> agent.run("Please find a remedy for hangover.")
```
Example with a Streamable HTTP MCP server:
```py
>>> with ToolCollection.from_mcp({"url": "http://127.0.0.1:8000/mcp", "transport": "streamable-http"}, trust_remote_code=True) as tool_collection:
>>> agent = CodeAgent(tools=[*tool_collection.tools], add_base_tools=True, model=model)
>>> agent.run("Please find a remedy for hangover.")
```
"""
try:
from mcpadapt.core import MCPAdapt
from mcpadapt.smolagents_adapter import SmolAgentsAdapter
except ImportError:
raise ImportError(
"""Please install 'mcp' extra to use ToolCollection.from_mcp: `pip install "smolagents[mcp]"`."""
)
if isinstance(server_parameters, dict):
transport = server_parameters.get("transport")
if transport is None:
warnings.warn(
"Passing a dict as server_parameters without specifying the 'transport' key is deprecated. "
"For now, it defaults to the legacy 'sse' (HTTP+SSE) transport, but this default will change "
"to 'streamable-http' in version 1.20. Please add the 'transport' key explicitly. ",
FutureWarning,
)
transport = "sse"
server_parameters["transport"] = transport
if transport not in {"sse", "streamable-http"}:
raise ValueError(
f"Unsupported transport: {transport}. Supported transports are 'streamable-http' and 'sse'."
)
if not trust_remote_code:
raise ValueError(
"Loading tools from MCP requires you to acknowledge you trust the MCP server, "
"as it will execute code on your local machine: pass `trust_remote_code=True`."
)
with MCPAdapt(server_parameters, SmolAgentsAdapter()) as tools:
yield cls(tools)
def tool(tool_function: Callable) -> Tool:
"""
Convert a function into an instance of a dynamically created Tool subclass.
Args:
tool_function (`Callable`): Function to convert into a Tool subclass.
Should have type hints for each input and a type hint for the output.
Should also have a docstring including the description of the function
and an 'Args:' part where each argument is described.
"""
tool_json_schema = get_json_schema(tool_function)["function"]
if "return" not in tool_json_schema:
raise TypeHintParsingException("Tool return type not found: make sure your function has a return type hint!")
class SimpleTool(Tool):
def __init__(self):
self.is_initialized = True
# Set the class attributes
SimpleTool.name = tool_json_schema["name"]
SimpleTool.description = tool_json_schema["description"]
SimpleTool.inputs = tool_json_schema["parameters"]["properties"]
SimpleTool.output_type = tool_json_schema["return"]["type"]
@wraps(tool_function)
def wrapped_function(*args, **kwargs):
return tool_function(*args, **kwargs)
# Bind the copied function to the forward method
SimpleTool.forward = staticmethod(wrapped_function)
# Get the signature parameters of the tool function
sig = inspect.signature(tool_function)
# - Add "self" as first parameter to tool_function signature
new_sig = sig.replace(
parameters=[inspect.Parameter("self", inspect.Parameter.POSITIONAL_OR_KEYWORD)] + list(sig.parameters.values())
)
# - Set the signature of the forward method
SimpleTool.forward.__signature__ = new_sig
# Create and attach the source code of the dynamically created tool class and forward method
# - Get the source code of tool_function
tool_source = inspect.getsource(tool_function)
# - Remove the tool decorator and function definition line
tool_source_body = "\n".join(tool_source.split("\n")[2:])
# - Dedent
tool_source_body = textwrap.dedent(tool_source_body)
# - Create the forward method source, including def line and indentation
forward_method_source = f"def forward{str(new_sig)}:\n{textwrap.indent(tool_source_body, ' ')}"
# - Create the class source
class_source = (
textwrap.dedent(f"""
class SimpleTool(Tool):
name: str = "{tool_json_schema["name"]}"
description: str = {json.dumps(textwrap.dedent(tool_json_schema["description"]).strip())}
inputs: dict[str, dict[str, str]] = {tool_json_schema["parameters"]["properties"]}
output_type: str = "{tool_json_schema["return"]["type"]}"
def __init__(self):
self.is_initialized = True
""")
+ textwrap.indent(forward_method_source, " ") # indent for class method
)
# - Store the source code on both class and method for inspection
SimpleTool.__source__ = class_source
SimpleTool.forward.__source__ = forward_method_source
simple_tool = SimpleTool()
return simple_tool
class PipelineTool(Tool):
"""
A [`Tool`] tailored towards Transformer models. On top of the class attributes of the base class [`Tool`], you will
need to specify:
- **model_class** (`type`) -- The class to use to load the model in this tool.
- **default_checkpoint** (`str`) -- The default checkpoint that should be used when the user doesn't specify one.
- **pre_processor_class** (`type`, *optional*, defaults to [`transformers.AutoProcessor`]) -- The class to use to load the
pre-processor
- **post_processor_class** (`type`, *optional*, defaults to [`transformers.AutoProcessor`]) -- The class to use to load the
post-processor (when different from the pre-processor).
Args:
model (`str` or [`transformers.PreTrainedModel`], *optional*):
The name of the checkpoint to use for the model, or the instantiated model. If unset, will default to the
value of the class attribute `default_checkpoint`.
pre_processor (`str` or `Any`, *optional*):
The name of the checkpoint to use for the pre-processor, or the instantiated pre-processor (can be a
tokenizer, an image processor, a feature extractor or a processor). Will default to the value of `model` if
unset.
post_processor (`str` or `Any`, *optional*):
The name of the checkpoint to use for the post-processor, or the instantiated pre-processor (can be a
tokenizer, an image processor, a feature extractor or a processor). Will default to the `pre_processor` if
unset.
device (`int`, `str` or `torch.device`, *optional*):
The device on which to execute the model. Will default to any accelerator available (GPU, MPS etc...), the
CPU otherwise.
device_map (`str` or `dict`, *optional*):
If passed along, will be used to instantiate the model.
model_kwargs (`dict`, *optional*):
Any keyword argument to send to the model instantiation.
token (`str`, *optional*):
The token to use as HTTP bearer authorization for remote files. If unset, will use the token generated when
running `huggingface-cli login` (stored in `~/.huggingface`).
hub_kwargs (additional keyword arguments, *optional*):
Any additional keyword argument to send to the methods that will load the data from the Hub.
"""
pre_processor_class = None
model_class = None
post_processor_class = None
default_checkpoint = None
description = "This is a pipeline tool"
name = "pipeline"
inputs = {"prompt": str}
output_type = str
skip_forward_signature_validation = True
def __init__(
self,
model=None,
pre_processor=None,
post_processor=None,
device=None,
device_map=None,
model_kwargs=None,
token=None,
**hub_kwargs,
):
if not _is_package_available("accelerate") or not _is_package_available("torch"):
raise ModuleNotFoundError(
"Please install 'transformers' extra to use a PipelineTool: `pip install 'smolagents[transformers]'`"
)
if model is None:
if self.default_checkpoint is None:
raise ValueError("This tool does not implement a default checkpoint, you need to pass one.")
model = self.default_checkpoint
if pre_processor is None:
pre_processor = model
self.model = model
self.pre_processor = pre_processor
self.post_processor = post_processor
self.device = device
self.device_map = device_map
self.model_kwargs = {} if model_kwargs is None else model_kwargs
if device_map is not None:
self.model_kwargs["device_map"] = device_map
self.hub_kwargs = hub_kwargs
self.hub_kwargs["token"] = token
super().__init__()
def setup(self):
"""
Instantiates the `pre_processor`, `model` and `post_processor` if necessary.
"""
if isinstance(self.pre_processor, str):
if self.pre_processor_class is None:
from transformers import AutoProcessor
self.pre_processor_class = AutoProcessor
self.pre_processor = self.pre_processor_class.from_pretrained(self.pre_processor, **self.hub_kwargs)
if isinstance(self.model, str):
self.model = self.model_class.from_pretrained(self.model, **self.model_kwargs, **self.hub_kwargs)
if self.post_processor is None:
self.post_processor = self.pre_processor
elif isinstance(self.post_processor, str):
if self.post_processor_class is None:
from transformers import AutoProcessor
self.post_processor_class = AutoProcessor
self.post_processor = self.post_processor_class.from_pretrained(self.post_processor, **self.hub_kwargs)
if self.device is None:
if self.device_map is not None:
self.device = list(self.model.hf_device_map.values())[0]
else:
from accelerate import PartialState
self.device = PartialState().default_device
if self.device_map is None:
self.model.to(self.device)
super().setup()
def encode(self, raw_inputs):
"""
Uses the `pre_processor` to prepare the inputs for the `model`.
"""
return self.pre_processor(raw_inputs)
def forward(self, inputs):
"""
Sends the inputs through the `model`.
"""
import torch
with torch.no_grad():
return self.model(**inputs)
def decode(self, outputs):
"""
Uses the `post_processor` to decode the model output.
"""
return self.post_processor(outputs)
def __call__(self, *args, sanitize_inputs_outputs: bool = False, **kwargs):
import torch
from accelerate.utils import send_to_device
if not self.is_initialized:
self.setup()
if sanitize_inputs_outputs:
args, kwargs = handle_agent_input_types(*args, **kwargs)
encoded_inputs = self.encode(*args, **kwargs)
tensor_inputs = {k: v for k, v in encoded_inputs.items() if isinstance(v, torch.Tensor)}
non_tensor_inputs = {k: v for k, v in encoded_inputs.items() if not isinstance(v, torch.Tensor)}
encoded_inputs = send_to_device(tensor_inputs, self.device)
outputs = self.forward({**encoded_inputs, **non_tensor_inputs})
outputs = send_to_device(outputs, "cpu")
decoded_outputs = self.decode(outputs)
if sanitize_inputs_outputs:
decoded_outputs = handle_agent_output_types(decoded_outputs, self.output_type)
return decoded_outputs
def get_tools_definition_code(tools: dict[str, Tool]) -> str:
tool_codes = []
for tool in tools.values():
validate_tool_attributes(tool.__class__, check_imports=False)
tool_code = instance_to_source(tool, base_cls=Tool)
tool_code = tool_code.replace("from smolagents.tools import Tool", "")
tool_code += f"\n\n{tool.name} = {tool.__class__.__name__}()\n"
tool_codes.append(tool_code)
tool_definition_code = "\n".join([f"import {module}" for module in BASE_BUILTIN_MODULES])
tool_definition_code += textwrap.dedent(
"""
from typing import Any
class Tool:
def __call__(self, *args, **kwargs):
return self.forward(*args, **kwargs)
def forward(self, *args, **kwargs):
pass # to be implemented in child class
"""
)
tool_definition_code += "\n\n".join(tool_codes)
return tool_definition_code
def validate_tool_arguments(tool: Tool, arguments: Any) -> str | None:
if isinstance(arguments, dict):
for key, value in arguments.items():
if key not in tool.inputs:
return f"Argument {key} is not in the tool's input schema."
parsed_type = _get_json_schema_type(type(value))["type"]
if parsed_type != tool.inputs[key]["type"] and not tool.inputs[key]["type"] == "any":
return f"Argument {key} has type '{parsed_type}' but should be '{tool.inputs[key]['type']}'."
for key in tool.inputs:
if key not in arguments:
return f"Argument {key} is required."
return None
else:
expected_type = list(tool.inputs.values())[0]["type"]
if _get_json_schema_type(type(arguments))["type"] != expected_type and not expected_type == "any":
return f"Argument has type '{type(arguments).__name__}' but should be '{expected_type}'."
return None
__all__ = [
"AUTHORIZED_TYPES",
"Tool",
"tool",
"load_tool",
"launch_gradio_demo",
"ToolCollection",
]