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feat: implement experimental AgentConfig and ToolPool with natural API
- Add experimental AgentConfig with toAgent() method for creating Agent instances - Implement ToolPool for managing collections of tools with clean constructor API - Support direct tool function passing: ToolPool([calculator, current_time]) - Add ToolPool.from_module() for importing entire tool modules - Resolve circular import issues with deferred imports - Add comprehensive test coverage for experimental features - Clean up obsolete code and maintain backward compatibility - Remove hacky path manipulation from tests, follow standard import patterns 🤖 Assisted by Amazon Q Developer
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12 files changed

+4500
-342
lines changed

12 files changed

+4500
-342
lines changed

pyproject.toml

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@@ -255,3 +255,10 @@ style = [
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["text", ""],
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["disabled", "fg:#858585 italic"]
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]
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[dependency-groups]
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dev = [
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"moto>=5.1.13",
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"pytest>=8.4.2",
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"pytest-asyncio>=1.1.1",
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]

src/strands/agent/agent.py

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@@ -57,7 +57,6 @@
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from ..types.tools import ToolResult, ToolUse
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from ..types.traces import AttributeValue
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from .agent_result import AgentResult
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from .config import AgentConfig
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from .conversation_manager import (
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ConversationManager,
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SlidingWindowConversationManager,
@@ -219,7 +218,6 @@ def __init__(
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load_tools_from_directory: bool = False,
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trace_attributes: Optional[Mapping[str, AttributeValue]] = None,
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*,
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config: Optional[Union[str, dict[str, Any]]] = None,
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agent_id: Optional[str] = None,
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name: Optional[str] = None,
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description: Optional[str] = None,
@@ -257,9 +255,6 @@ def __init__(
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load_tools_from_directory: Whether to load and automatically reload tools in the `./tools/` directory.
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Defaults to False.
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trace_attributes: Custom trace attributes to apply to the agent's trace span.
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config: Path to agent configuration file (JSON) or configuration dictionary.
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Supports agent-format.md specification with fields: tools, model, prompt.
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Constructor parameters override config file values when both are provided.
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agent_id: Optional ID for the agent, useful for session management and multi-agent scenarios.
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Defaults to "default".
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name: name of the Agent
@@ -277,24 +272,6 @@ def __init__(
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Raises:
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ValueError: If agent id contains path separators.
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"""
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# Load configuration if provided and merge with constructor parameters
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# Constructor parameters take precedence over config file values
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if config is not None:
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try:
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agent_config = AgentConfig(config)
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# Apply config values only if constructor parameters are None
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if model is None:
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model = agent_config.model
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if tools is None:
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config_tools = agent_config.tools
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if config_tools is not None:
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tools = list(config_tools) # Cast List[str] to list[Union[str, dict[str, str], Any]]
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if system_prompt is None:
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system_prompt = agent_config.system_prompt
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except (FileNotFoundError, json.JSONDecodeError, ValueError) as e:
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raise ValueError(f"Failed to load agent configuration: {e}") from e
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self.model = BedrockModel() if not model else BedrockModel(model_id=model) if isinstance(model, str) else model
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self.messages = messages if messages is not None else []
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src/strands/agent/config.py

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This file was deleted.

src/strands/experimental/__init__.py

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@@ -2,3 +2,8 @@
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This module implements experimental features that are subject to change in future revisions without notice.
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"""
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from .agent_config import AgentConfig
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from .tool_pool import ToolPool
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__all__ = ["AgentConfig", "ToolPool"]
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@@ -0,0 +1,85 @@
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# ABOUTME: Experimental agent configuration with toAgent() method for creating Agent instances
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# ABOUTME: Extends core AgentConfig with experimental instantiation patterns using ToolPool
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"""Experimental agent configuration with enhanced instantiation patterns."""
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import json
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from typing import Any, Optional, Union, TYPE_CHECKING
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# Avoid circular import: experimental/agent_config.py -> agent.agent ->
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# event_loop.event_loop -> experimental.hooks -> experimental.__init__.py -> AgentConfig
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if TYPE_CHECKING:
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from ..agent.agent import Agent
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class AgentConfig:
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"""Agent configuration with toAgent() method and ToolPool integration."""
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def __init__(self, config_source: Union[str, dict[str, Any]]):
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"""Initialize AgentConfig from file path or dictionary.
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Args:
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config_source: Path to JSON config file or config dictionary
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"""
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if isinstance(config_source, str):
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# Load from file
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with open(config_source, 'r') as f:
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config_data = json.load(f)
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else:
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# Use dictionary directly
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config_data = config_source
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self.model = config_data.get('model')
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self.tools = config_data.get('tools')
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self.system_prompt = config_data.get('system_prompt') or config_data.get('prompt')
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# Create empty default ToolPool
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from .tool_pool import ToolPool
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self._tool_pool = ToolPool()
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@property
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def tool_pool(self) -> "ToolPool":
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"""Get the tool pool for this configuration.
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Returns:
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ToolPool instance
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"""
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return self._tool_pool
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def toAgent(self, tools: Optional["ToolPool"] = None, **kwargs: Any) -> "Agent":
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"""Create an Agent instance from this configuration.
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Args:
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tools: ToolPool to use (overrides default empty pool)
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**kwargs: Additional parameters to override config values.
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Supports all Agent constructor parameters.
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Returns:
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Configured Agent instance
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Example:
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config = AgentConfig({"model": "anthropic.claude-3-5-sonnet-20241022-v2:0"})
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pool = ToolPool()
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pool.add_tool_function(calculator)
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agent = config.toAgent(tools=pool)
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"""
65+
# Import here to avoid circular imports:
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# experimental/agent_config.py -> agent.agent -> event_loop.event_loop ->
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# experimental.hooks -> experimental.__init__.py -> AgentConfig
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from ..agent.agent import Agent
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# Start with config values
71+
agent_params = {}
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if self.model is not None:
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agent_params['model'] = self.model
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if self.system_prompt is not None:
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agent_params['system_prompt'] = self.system_prompt
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# Use provided ToolPool or default empty one
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tool_pool = tools if tools is not None else self._tool_pool
80+
agent_params['tools'] = tool_pool.to_agent_tools()
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# Override with any other provided kwargs
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agent_params.update(kwargs)
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return Agent(**agent_params)
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@@ -0,0 +1,110 @@
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# ABOUTME: Tool pool system using existing AgentTool base class for tool selection and management
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# ABOUTME: Integrates with existing tool infrastructure and @tool decorator pattern
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"""Experimental tool pool system for structured tool management."""
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5+
from typing import Any, Optional, Union, Callable
6+
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from ..types.tools import AgentTool
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from ..tools.tools import PythonAgentTool
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from ..tools.decorator import DecoratedFunctionTool
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class ToolPool:
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"""Pool of available tools for agent selection using existing tool infrastructure."""
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def __init__(self, tools: Optional[list[Union[AgentTool, Callable]]] = None):
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"""Initialize tool pool.
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Args:
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tools: List of AgentTool instances or @tool decorated functions
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"""
21+
self._tools: dict[str, AgentTool] = {}
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if tools:
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for tool in tools:
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if isinstance(tool, AgentTool):
25+
self.add_tool(tool)
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elif callable(tool):
27+
self.add_tool_function(tool)
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else:
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raise ValueError(f"Tool must be AgentTool instance or callable, got {type(tool)}")
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31+
def add_tool(self, tool: AgentTool) -> None:
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"""Add existing AgentTool instance to the pool.
33+
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Args:
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tool: AgentTool instance to add
36+
"""
37+
self._tools[tool.tool_name] = tool
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39+
def add_tool_function(self, tool_func: Callable) -> None:
40+
"""Add @tool decorated function to the pool.
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Args:
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tool_func: Function decorated with @tool
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"""
45+
if hasattr(tool_func, '_strands_tool_spec'):
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# This is a decorated function tool
47+
tool_spec = tool_func._strands_tool_spec
48+
tool_name = tool_spec.get('name', tool_func.__name__)
49+
decorated_tool = DecoratedFunctionTool(
50+
tool_name=tool_name,
51+
tool_spec=tool_spec,
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tool_func=tool_func,
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metadata={}
54+
)
55+
self.add_tool(decorated_tool)
56+
else:
57+
raise ValueError(f"Function {tool_func.__name__} is not decorated with @tool")
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59+
def add_tools_from_module(self, module: Any) -> None:
60+
"""Add all @tool decorated functions from a Python module.
61+
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Args:
63+
module: Python module containing @tool decorated functions
64+
"""
65+
import inspect
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67+
for name, obj in inspect.getmembers(module):
68+
if inspect.isfunction(obj) and hasattr(obj, '_strands_tool_spec'):
69+
self.add_tool_function(obj)
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71+
@classmethod
72+
def from_module(cls, module: Any) -> "ToolPool":
73+
"""Create ToolPool from all @tool functions in a module.
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Args:
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module: Python module containing @tool decorated functions
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Returns:
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ToolPool with all tools from the module
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"""
81+
pool = cls()
82+
pool.add_tools_from_module(module)
83+
return pool
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85+
def get_tool(self, name: str) -> Optional[AgentTool]:
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"""Get tool by name.
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Args:
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name: Tool name
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91+
Returns:
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AgentTool if found, None otherwise
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"""
94+
return self._tools.get(name)
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96+
def list_tools(self) -> list[str]:
97+
"""List available tool names.
98+
99+
Returns:
100+
List of tool names
101+
"""
102+
return list(self._tools.keys())
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104+
def to_agent_tools(self) -> list[AgentTool]:
105+
"""Convert to format compatible with Agent tools parameter.
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107+
Returns:
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List of AgentTool instances
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"""
110+
return list(self._tools.values())

src/strands/tools/registry.py

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@@ -55,23 +55,8 @@ def process_tools(self, tools: List[Any]) -> List[str]:
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tool_names = []
5656

5757
def add_tool(tool: Any) -> None:
58-
# Case 1: String - could be file path or tool name from strands_tools
58+
# Case 1: String file path
5959
if isinstance(tool, str):
60-
# First try to import from strands_tools.{tool_name}
61-
try:
62-
import importlib
63-
64-
module_name = f"strands_tools.{tool}"
65-
tool_module = importlib.import_module(module_name)
66-
if hasattr(tool_module, tool):
67-
tool_obj = getattr(tool_module, tool)
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# Recursively process the imported tool object
69-
add_tool(tool_obj)
70-
return
71-
except (ImportError, AttributeError):
72-
pass
73-
74-
# If not found in strands_tools, treat as file path
7560
# Extract tool name from path
7661
tool_name = os.path.basename(tool).split(".")[0]
7762
self.load_tool_from_filepath(tool_name=tool_name, tool_path=tool)

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