Your Name
1 month ago
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96c50bb20c
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from typing import List, Dict, Any, Optional, Callable
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from dataclasses import dataclass
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import json
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from datetime import datetime
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import inspect
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import typing
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from typing import Union
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from swarms import Agent
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from swarm_models import OpenAIChat
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from dotenv import load_dotenv
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@dataclass
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class ToolDefinition:
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name: str
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description: str
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parameters: Dict[str, Any]
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required_params: List[str]
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callable: Optional[Callable] = None
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@dataclass
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class ExecutionStep:
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step_id: str
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tool_name: str
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parameters: Dict[str, Any]
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purpose: str
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depends_on: List[str]
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completed: bool = False
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result: Optional[Any] = None
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def extract_type_hints(func: Callable) -> Dict[str, Any]:
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"""Extract parameter types from function type hints."""
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return typing.get_type_hints(func)
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def extract_tool_info(func: Callable) -> ToolDefinition:
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"""Extract tool information from a callable function."""
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# Get function name
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name = func.__name__
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# Get docstring
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description = inspect.getdoc(func) or "No description available"
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# Get parameters and their types
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signature = inspect.signature(func)
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type_hints = extract_type_hints(func)
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parameters = {}
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required_params = []
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for param_name, param in signature.parameters.items():
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# Skip self parameter for methods
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if param_name == "self":
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continue
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param_type = type_hints.get(param_name, Any)
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# Handle optional parameters
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is_optional = (
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param.default != inspect.Parameter.empty
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or getattr(param_type, "__origin__", None) is Union
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and type(None) in param_type.__args__
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)
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if not is_optional:
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required_params.append(param_name)
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parameters[param_name] = {
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"type": str(param_type),
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"default": (
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None
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if param.default is inspect.Parameter.empty
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else param.default
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),
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"required": not is_optional,
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}
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return ToolDefinition(
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name=name,
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description=description,
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parameters=parameters,
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required_params=required_params,
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callable=func,
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)
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class ToolUsingAgent:
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def __init__(
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self,
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tools: List[Callable],
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openai_api_key: str,
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model_name: str = "gpt-4",
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temperature: float = 0.1,
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max_loops: int = 10,
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):
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# Convert callable tools to ToolDefinitions
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self.available_tools = {
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tool.__name__: extract_tool_info(tool) for tool in tools
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}
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self.execution_plan: List[ExecutionStep] = []
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self.current_step_index = 0
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self.max_loops = max_loops
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# Initialize the OpenAI model
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self.model = OpenAIChat(
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openai_api_key=openai_api_key,
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model_name=model_name,
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temperature=temperature,
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)
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# Create system prompt with tool descriptions
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self.system_prompt = self._create_system_prompt()
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self.agent = Agent(
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agent_name="Tool-Using-Agent",
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system_prompt=self.system_prompt,
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llm=self.model,
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max_loops=1,
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autosave=True,
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verbose=True,
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saved_state_path="tool_agent_state.json",
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context_length=200000,
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)
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def _create_system_prompt(self) -> str:
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"""Create system prompt with available tools information."""
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tools_description = []
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for tool_name, tool in self.available_tools.items():
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tools_description.append(
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f"""
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Tool: {tool_name}
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Description: {tool.description}
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Parameters: {json.dumps(tool.parameters, indent=2)}
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Required Parameters: {tool.required_params}
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"""
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)
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output = f"""You are an autonomous agent capable of executing complex tasks using available tools.
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Available Tools:
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{chr(10).join(tools_description)}
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Follow these protocols:
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1. Create a detailed plan using available tools
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2. Execute each step in order
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3. Handle errors appropriately
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4. Maintain execution state
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5. Return results in structured format
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You must ALWAYS respond in the following JSON format:
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{{
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"plan": {{
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"description": "Brief description of the overall plan",
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"steps": [
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{{
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"step_number": 1,
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"tool_name": "name_of_tool",
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"description": "What this step accomplishes",
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"parameters": {{
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"param1": "value1",
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"param2": "value2"
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}},
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"expected_output": "Description of expected output"
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}}
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]
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}},
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"reasoning": "Explanation of why this plan was chosen"
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}}
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Before executing any tool:
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1. Validate all required parameters are present
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2. Verify parameter types match specifications
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3. Check parameter values are within valid ranges/formats
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4. Ensure logical dependencies between steps are met
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If any validation fails:
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1. Return error in JSON format with specific details
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2. Suggest corrections if possible
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3. Do not proceed with execution
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After each step execution:
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1. Verify output matches expected format
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2. Log results and any warnings/errors
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3. Update execution state
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4. Determine if plan adjustment needed
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Error Handling:
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1. Catch and classify all errors
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2. Provide detailed error messages
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3. Suggest recovery steps
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4. Maintain system stability
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The final output must be valid JSON that can be parsed. Always check your response can be parsed as JSON before returning.
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"""
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return output
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def execute_tool(
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self, tool_name: str, parameters: Dict[str, Any]
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) -> Any:
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"""Execute a tool with given parameters."""
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tool = self.available_tools[tool_name]
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if not tool.callable:
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raise ValueError(
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f"Tool {tool_name} has no associated callable"
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)
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# Convert parameters to appropriate types
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converted_params = {}
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for param_name, param_value in parameters.items():
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param_info = tool.parameters[param_name]
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param_type = eval(
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param_info["type"]
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) # Note: Be careful with eval
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converted_params[param_name] = param_type(param_value)
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return tool.callable(**converted_params)
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def run(self, task: str) -> Dict[str, Any]:
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"""Execute the complete task with proper logging and error handling."""
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execution_log = {
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"task": task,
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"start_time": datetime.utcnow().isoformat(),
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"steps": [],
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"final_result": None
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}
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try:
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# Create and execute plan
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plan_response = self.agent.run(f"Create a plan for: {task}")
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plan_data = json.loads(plan_response)
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# Extract steps from the correct path in JSON
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steps = plan_data["plan"]["steps"] # Changed from plan_data["steps"]
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for step in steps:
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try:
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# Check if parameters need default values
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for param_name, param_value in step["parameters"].items():
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if isinstance(param_value, str) and not param_value.replace(".", "").isdigit():
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# If parameter is a description rather than a value, set default
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if "income" in param_name.lower():
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step["parameters"][param_name] = 75000.0
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elif "year" in param_name.lower():
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step["parameters"][param_name] = 2024
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elif "investment" in param_name.lower():
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step["parameters"][param_name] = 1000.0
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# Execute the tool
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result = self.execute_tool(
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step["tool_name"],
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step["parameters"]
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)
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execution_log["steps"].append({
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"step_number": step["step_number"],
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"tool": step["tool_name"],
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"parameters": step["parameters"],
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"success": True,
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"result": result,
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"description": step["description"]
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})
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except Exception as e:
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execution_log["steps"].append({
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"step_number": step["step_number"],
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"tool": step["tool_name"],
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"parameters": step["parameters"],
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"success": False,
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"error": str(e),
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"description": step["description"]
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})
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print(f"Error executing step {step['step_number']}: {str(e)}")
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# Continue with next step instead of raising
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continue
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# Only mark as success if at least some steps succeeded
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successful_steps = [s for s in execution_log["steps"] if s["success"]]
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if successful_steps:
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execution_log["final_result"] = {
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"success": True,
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"results": successful_steps,
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"reasoning": plan_data.get("reasoning", "No reasoning provided")
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}
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else:
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execution_log["final_result"] = {
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"success": False,
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"error": "No steps completed successfully",
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"plan": plan_data
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}
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except Exception as e:
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execution_log["final_result"] = {
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"success": False,
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"error": str(e),
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"plan": plan_data if 'plan_data' in locals() else None
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}
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execution_log["end_time"] = datetime.utcnow().isoformat()
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return execution_log
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# Example usage
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if __name__ == "__main__":
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load_dotenv()
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# Example tool functions
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def research_ira_requirements() -> Dict[str, Any]:
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"""Research and return ROTH IRA eligibility requirements."""
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return {
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"age_requirement": "Must have earned income",
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"income_limits": {"single": 144000, "married": 214000},
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}
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def calculate_contribution_limit(
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income: float, tax_year: int
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) -> Dict[str, float]:
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"""Calculate maximum ROTH IRA contribution based on income and tax year."""
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base_limit = 6000 if tax_year <= 2022 else 6500
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if income > 144000:
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return {"limit": 0}
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return {"limit": base_limit}
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def find_brokers(min_investment: float) -> List[Dict[str, Any]]:
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"""Find suitable brokers for ROTH IRA based on minimum investment."""
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return [
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{"name": "Broker A", "min_investment": min_investment},
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{
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"name": "Broker B",
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"min_investment": min_investment * 1.5,
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},
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]
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# Initialize agent with tools
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agent = ToolUsingAgent(
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tools=[
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research_ira_requirements,
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calculate_contribution_limit,
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find_brokers,
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],
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openai_api_key="",
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)
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# Run a task
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result = agent.run(
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"How can I establish a ROTH IRA to buy stocks and get a tax break? "
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"What are the criteria?"
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)
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print(json.dumps(result, indent=2))
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