Feat/agent upgrade

evoagentx/actions/action.py

@@ -1,14 +1,14 @@
 import json
 from pydantic import model_validator 
 from pydantic_core import PydanticUndefined
-from typing import Optional, Type, Tuple, Union, List, Any
+from typing import ClassVar, Optional, Type, Tuple, Union, List, Any
 
 from ..core.module import BaseModule
 from ..core.registry import MODULE_REGISTRY
 from ..core.parser import Parser
 from ..core.message import Message
 from ..models.base_model import BaseLLM, LLMOutputParser
-from ..tools.tool import Toolkit 
+from ..tools.tool import Tool, Toolkit 
 from ..prompts.context_extraction import CONTEXT_EXTRACTION
 from ..prompts.template import PromptTemplate  
 
@@ -53,15 +53,15 @@ class ActionOutput(LLMOutputParser):
     to convert the output to structured data. It inherits from LLMOutputParser
     to support parsing of LLM outputs into structured action results.
     """
+    fix_json_schema_error: ClassVar[bool] = True
 
     def to_str(self) -> str:
         """Convert the output to a formatted JSON string.
 
         Returns:
             A pretty-printed JSON string representation of the structured data.
         """
-        return json.dumps(self.get_structured_data(), indent=4)
-
+        return json.dumps(self.get_structured_data(), indent=4, ensure_ascii=False)
 
 class Action(BaseModule):
     """Base class for all actions in the EvoAgentX framework.
@@ -83,7 +83,7 @@ class Action(BaseModule):
     description: str
     prompt: Optional[str] = None
     prompt_template: Optional[PromptTemplate] = None 
-    tools: Optional[List[Toolkit]] = None # specify the possible tool for the action
+    tools: Optional[List[Union[Tool, Toolkit]]] = None # specify the possible tool for the action
     inputs_format: Optional[Type[ActionInput]] = None # specify the input format of the action
     outputs_format: Optional[Type[Parser]] = None  # specify the possible structured output format
 
@@ -211,4 +211,4 @@ def execute(self, llm: Optional[BaseLLM] = None, action: Action = None, context:
         )
         action_inputs_data = action_inputs.get_structured_data()
 
-        return action_inputs_data
+        return action_inputs_data

evoagentx/actions/agent_generation.py

@@ -182,7 +182,7 @@ def execute(self, llm: Optional[BaseLLM] = None, inputs: Optional[dict] = None,
                 }
                 for tool in self.tools
             ]
-            prompt_params_values["tools"] = AGENT_GENERATION_TOOLS_PROMPT.format(tools_description=tool_description)
+            prompt_params_values["tools"] = AGENT_GENERATION_TOOLS_PROMPT.format(tool_descriptions=tool_description)
         prompt = self.prompt.format(**prompt_params_values)
         agents = llm.generate(
             prompt = prompt,