fixed some bugs

Author: DVampire

examples/run_example.py

@@ -22,7 +22,7 @@ async def main():
     logger.info(f"Load config: {config}")
 
     # Registed models
-    model_manager.init_models(use_local_proxy=False)
+    model_manager.init_models(use_local_proxy=True)
     logger.info("Registed models: %s", ", ".join(model_manager.registed_models.keys()))
 
     # Create agent

src/config/cfg.py

@@ -21,7 +21,7 @@ class SearcherToolConfig(BaseModel):
     max_length: int = Field(default=50000, description="Maximum character length for the content to be fetched")
 
 class DeepResearcherToolConfig(BaseModel):
-    model_id: str = Field(default="claude-4-sonnet", description="Model ID for the LLM to use")
+    model_id: str = Field(default="gpt-4.1", description="Model ID for the LLM to use")
     max_depth: int = Field(default=2, description="Maximum depth for the search")
     max_insights: int = Field(default=20, description="Maximum number of insights to extract")
     time_limit_seconds: int = Field(default=60, description="Time limit for the search in seconds")
@@ -48,7 +48,7 @@ class DeepAnalyzerToolConfig(BaseModel):
     summarizer_model_id: str = Field(default="gemini-2.5-pro", description="Model ID for the LLM to use")
 
 class AgentConfig(BaseModel):
-    model_id: str = Field(default="claude-4-sonnet",
+    model_id: str = Field(default="gpt-4.1",
                           description="Model ID for the LLM to use")
     name: str = Field(default="agent", 
                       description="Name of the agent")
@@ -64,10 +64,10 @@ class AgentConfig(BaseModel):
                                       description="List of agents the agent can manage")
 
 class HierarchicalAgentConfig(BaseModel):
-    name: str = Field(default="agentscope", description="Name of the hierarchical agent")
+    name: str = Field(default="dra", description="Name of the hierarchical agent")
     use_hierarchical_agent: bool = Field(default=True, description="Whether to use hierarchical agent")
     planning_agent_config: AgentConfig = Field(default_factory=lambda: AgentConfig(
-        model_id="claude-4-sonnet",
+        model_id="gpt-4.1",
         name="planning_agent",
         description="A planning agent that can plan the steps to complete the task.",
         max_steps=20,
@@ -76,7 +76,7 @@ class HierarchicalAgentConfig(BaseModel):
         managed_agents=["deep_analyzer_agent", "browser_use_agent", "deep_researcher_agent"],
     ))
     deep_analyzer_agent_config: AgentConfig = Field(default_factory=lambda: AgentConfig(
-        model_id="claude-4-sonnet",
+        model_id="gpt-4.1",
         name="deep_analyzer_agent",
         description="A team member that that performs systematic, step-by-step analysis of a given task, optionally leveraging information from external resources such as attached file or uri to provide comprehensive reasoning and answers. For any tasks that require in-depth analysis, particularly those involving attached file or uri, game, chess, computational tasks, or any other complex tasks. Please ask him for the reasoning and the final answer.",
         max_steps=3,
@@ -92,7 +92,7 @@ class HierarchicalAgentConfig(BaseModel):
         tools=["auto_browser_use", "python_interpreter"],
     ))
     deep_researcher_agent_config: AgentConfig = Field(default_factory=lambda: AgentConfig(
-        model_id="claude-4-sonnet",
+        model_id="gpt-4.1",
         name="deep_researcher_agent",
         description="A team member capable of conducting extensive web searches to complete tasks, primarily focused on retrieving broad and preliminary information for quickly understanding a topic or obtaining rough answers. For tasks that require precise, structured, or interactive page-level information retrieval, please use the `browser_use_agent`.",
         max_steps=3,

src/tools/deep_researcher.py

@@ -566,7 +566,8 @@ async def _analyze_content(
         return insights
 
     async def _summary(self, query: str, reference_materials: str) -> str:
-        model = model_manager.registed_models["Qwen"]
+
+        model = model_manager.registed_models['gpt-4o-search-preview']
 
         messages = [
             {"role": "user", "content": query}

tests/test_models.py

@@ -21,7 +21,7 @@
         }
     ]
 
-    response = asyncio.run(model_manager.registed_models["claude-4-sonnet"](
+    response = asyncio.run(model_manager.registed_models["gpt-4.1"](
         messages=messages,
     ))
     print(response)
@@ -31,7 +31,3 @@
     model = model_manager.registed_models["langchain-gpt-4.1"]
     response = asyncio.run(model.ainvoke("What is the capital of France?"))
     print(response)
-
-    model = model_manager.registed_models["langchain-o3"]
-    response = asyncio.run(model.ainvoke("What is the capital of France?"))
-    print(response)

tests/test_python_interpreter.py

@@ -0,0 +1,35 @@
+import warnings
+warnings.simplefilter("ignore", DeprecationWarning)
+
+import os
+import sys
+from pathlib import Path
+import asyncio
+
+root = str(Path(__file__).resolve().parents[1])
+sys.path.append(root)
+
+from src.tools.python_interpreter import PythonInterpreterTool
+from src.models import model_manager
+
+if __name__ == "__main__":
+    model_manager.init_models(use_local_proxy=False)
+    
+    pit = PythonInterpreterTool()
+    code = """
+def fibonacci(n):
+    if n <= 0:
+        return []
+    elif n == 1:
+        return [0]
+    elif n == 2:
+        return [0, 1]
+    else:
+        fib_sequence = [0, 1]
+        for i in range(2, n):
+            fib_sequence.append(fib_sequence[-1] + fib_sequence[-2])
+        return fib_sequence
+result = fibonacci(10)
+    """
+    content = asyncio.run(pit.forward(code))
+    print(content)