app/bedrock.py

@@ -1,334 +0,0 @@
-import json
-import sys
-import time
-import uuid
-from datetime import datetime
-from typing import Dict, List, Literal, Optional
-
-import boto3
-
-
-# Global variables to track the current tool use ID across function calls
-# Tmp solution
-CURRENT_TOOLUSE_ID = None
-
-
-# Class to handle OpenAI-style response formatting
-class OpenAIResponse:
-    def __init__(self, data):
-        # Recursively convert nested dicts and lists to OpenAIResponse objects
-        for key, value in data.items():
-            if isinstance(value, dict):
-                value = OpenAIResponse(value)
-            elif isinstance(value, list):
-                value = [
-                    OpenAIResponse(item) if isinstance(item, dict) else item
-                    for item in value
-                ]
-            setattr(self, key, value)
-
-    def model_dump(self, *args, **kwargs):
-        # Convert object to dict and add timestamp
-        data = self.__dict__
-        data["created_at"] = datetime.now().isoformat()
-        return data
-
-
-# Main client class for interacting with Amazon Bedrock
-class BedrockClient:
-    def __init__(self):
-        # Initialize Bedrock client, you need to configure AWS env first
-        try:
-            self.client = boto3.client("bedrock-runtime")
-            self.chat = Chat(self.client)
-        except Exception as e:
-            print(f"Error initializing Bedrock client: {e}")
-            sys.exit(1)
-
-
-# Chat interface class
-class Chat:
-    def __init__(self, client):
-        self.completions = ChatCompletions(client)
-
-
-# Core class handling chat completions functionality
-class ChatCompletions:
-    def __init__(self, client):
-        self.client = client
-
-    def _convert_openai_tools_to_bedrock_format(self, tools):
-        # Convert OpenAI function calling format to Bedrock tool format
-        bedrock_tools = []
-        for tool in tools:
-            if tool.get("type") == "function":
-                function = tool.get("function", {})
-                bedrock_tool = {
-                    "toolSpec": {
-                        "name": function.get("name", ""),
-                        "description": function.get("description", ""),
-                        "inputSchema": {
-                            "json": {
-                                "type": "object",
-                                "properties": function.get("parameters", {}).get(
-                                    "properties", {}
-                                ),
-                                "required": function.get("parameters", {}).get(
-                                    "required", []
-                                ),
-                            }
-                        },
-                    }
-                }
-                bedrock_tools.append(bedrock_tool)
-        return bedrock_tools
-
-    def _convert_openai_messages_to_bedrock_format(self, messages):
-        # Convert OpenAI message format to Bedrock message format
-        bedrock_messages = []
-        system_prompt = []
-        for message in messages:
-            if message.get("role") == "system":
-                system_prompt = [{"text": message.get("content")}]
-            elif message.get("role") == "user":
-                bedrock_message = {
-                    "role": message.get("role", "user"),
-                    "content": [{"text": message.get("content")}],
-                }
-                bedrock_messages.append(bedrock_message)
-            elif message.get("role") == "assistant":
-                bedrock_message = {
-                    "role": "assistant",
-                    "content": [{"text": message.get("content")}],
-                }
-                openai_tool_calls = message.get("tool_calls", [])
-                if openai_tool_calls:
-                    bedrock_tool_use = {
-                        "toolUseId": openai_tool_calls[0]["id"],
-                        "name": openai_tool_calls[0]["function"]["name"],
-                        "input": json.loads(
-                            openai_tool_calls[0]["function"]["arguments"]
-                        ),
-                    }
-                    bedrock_message["content"].append({"toolUse": bedrock_tool_use})
-                    global CURRENT_TOOLUSE_ID
-                    CURRENT_TOOLUSE_ID = openai_tool_calls[0]["id"]
-                bedrock_messages.append(bedrock_message)
-            elif message.get("role") == "tool":
-                bedrock_message = {
-                    "role": "user",
-                    "content": [
-                        {
-                            "toolResult": {
-                                "toolUseId": CURRENT_TOOLUSE_ID,
-                                "content": [{"text": message.get("content")}],
-                            }
-                        }
-                    ],
-                }
-                bedrock_messages.append(bedrock_message)
-            else:
-                raise ValueError(f"Invalid role: {message.get('role')}")
-        return system_prompt, bedrock_messages
-
-    def _convert_bedrock_response_to_openai_format(self, bedrock_response):
-        # Convert Bedrock response format to OpenAI format
-        content = ""
-        if bedrock_response.get("output", {}).get("message", {}).get("content"):
-            content_array = bedrock_response["output"]["message"]["content"]
-            content = "".join(item.get("text", "") for item in content_array)
-        if content == "":
-            content = "."
-
-        # Handle tool calls in response
-        openai_tool_calls = []
-        if bedrock_response.get("output", {}).get("message", {}).get("content"):
-            for content_item in bedrock_response["output"]["message"]["content"]:
-                if content_item.get("toolUse"):
-                    bedrock_tool_use = content_item["toolUse"]
-                    global CURRENT_TOOLUSE_ID
-                    CURRENT_TOOLUSE_ID = bedrock_tool_use["toolUseId"]
-                    openai_tool_call = {
-                        "id": CURRENT_TOOLUSE_ID,
-                        "type": "function",
-                        "function": {
-                            "name": bedrock_tool_use["name"],
-                            "arguments": json.dumps(bedrock_tool_use["input"]),
-                        },
-                    }
-                    openai_tool_calls.append(openai_tool_call)
-
-        # Construct final OpenAI format response
-        openai_format = {
-            "id": f"chatcmpl-{uuid.uuid4()}",
-            "created": int(time.time()),
-            "object": "chat.completion",
-            "system_fingerprint": None,
-            "choices": [
-                {
-                    "finish_reason": bedrock_response.get("stopReason", "end_turn"),
-                    "index": 0,
-                    "message": {
-                        "content": content,
-                        "role": bedrock_response.get("output", {})
-                        .get("message", {})
-                        .get("role", "assistant"),
-                        "tool_calls": openai_tool_calls
-                        if openai_tool_calls != []
-                        else None,
-                        "function_call": None,
-                    },
-                }
-            ],
-            "usage": {
-                "completion_tokens": bedrock_response.get("usage", {}).get(
-                    "outputTokens", 0
-                ),
-                "prompt_tokens": bedrock_response.get("usage", {}).get(
-                    "inputTokens", 0
-                ),
-                "total_tokens": bedrock_response.get("usage", {}).get("totalTokens", 0),
-            },
-        }
-        return OpenAIResponse(openai_format)
-
-    async def _invoke_bedrock(
-        self,
-        model: str,
-        messages: List[Dict[str, str]],
-        max_tokens: int,
-        temperature: float,
-        tools: Optional[List[dict]] = None,
-        tool_choice: Literal["none", "auto", "required"] = "auto",
-        **kwargs,
-    ) -> OpenAIResponse:
-        # Non-streaming invocation of Bedrock model
-        (
-            system_prompt,
-            bedrock_messages,
-        ) = self._convert_openai_messages_to_bedrock_format(messages)
-        response = self.client.converse(
-            modelId=model,
-            system=system_prompt,
-            messages=bedrock_messages,
-            inferenceConfig={"temperature": temperature, "maxTokens": max_tokens},
-            toolConfig={"tools": tools} if tools else None,
-        )
-        openai_response = self._convert_bedrock_response_to_openai_format(response)
-        return openai_response
-
-    async def _invoke_bedrock_stream(
-        self,
-        model: str,
-        messages: List[Dict[str, str]],
-        max_tokens: int,
-        temperature: float,
-        tools: Optional[List[dict]] = None,
-        tool_choice: Literal["none", "auto", "required"] = "auto",
-        **kwargs,
-    ) -> OpenAIResponse:
-        # Streaming invocation of Bedrock model
-        (
-            system_prompt,
-            bedrock_messages,
-        ) = self._convert_openai_messages_to_bedrock_format(messages)
-        response = self.client.converse_stream(
-            modelId=model,
-            system=system_prompt,
-            messages=bedrock_messages,
-            inferenceConfig={"temperature": temperature, "maxTokens": max_tokens},
-            toolConfig={"tools": tools} if tools else None,
-        )
-
-        # Initialize response structure
-        bedrock_response = {
-            "output": {"message": {"role": "", "content": []}},
-            "stopReason": "",
-            "usage": {},
-            "metrics": {},
-        }
-        bedrock_response_text = ""
-        bedrock_response_tool_input = ""
-
-        # Process streaming response
-        stream = response.get("stream")
-        if stream:
-            for event in stream:
-                if event.get("messageStart", {}).get("role"):
-                    bedrock_response["output"]["message"]["role"] = event[
-                        "messageStart"
-                    ]["role"]
-                if event.get("contentBlockDelta", {}).get("delta", {}).get("text"):
-                    bedrock_response_text += event["contentBlockDelta"]["delta"]["text"]
-                    print(
-                        event["contentBlockDelta"]["delta"]["text"], end="", flush=True
-                    )
-                if event.get("contentBlockStop", {}).get("contentBlockIndex") == 0:
-                    bedrock_response["output"]["message"]["content"].append(
-                        {"text": bedrock_response_text}
-                    )
-                if event.get("contentBlockStart", {}).get("start", {}).get("toolUse"):
-                    bedrock_tool_use = event["contentBlockStart"]["start"]["toolUse"]
-                    tool_use = {
-                        "toolUseId": bedrock_tool_use["toolUseId"],
-                        "name": bedrock_tool_use["name"],
-                    }
-                    bedrock_response["output"]["message"]["content"].append(
-                        {"toolUse": tool_use}
-                    )
-                    global CURRENT_TOOLUSE_ID
-                    CURRENT_TOOLUSE_ID = bedrock_tool_use["toolUseId"]
-                if event.get("contentBlockDelta", {}).get("delta", {}).get("toolUse"):
-                    bedrock_response_tool_input += event["contentBlockDelta"]["delta"][
-                        "toolUse"
-                    ]["input"]
-                    print(
-                        event["contentBlockDelta"]["delta"]["toolUse"]["input"],
-                        end="",
-                        flush=True,
-                    )
-                if event.get("contentBlockStop", {}).get("contentBlockIndex") == 1:
-                    bedrock_response["output"]["message"]["content"][1]["toolUse"][
-                        "input"
-                    ] = json.loads(bedrock_response_tool_input)
-        print()
-        openai_response = self._convert_bedrock_response_to_openai_format(
-            bedrock_response
-        )
-        return openai_response
-
-    def create(
-        self,
-        model: str,
-        messages: List[Dict[str, str]],
-        max_tokens: int,
-        temperature: float,
-        stream: Optional[bool] = True,
-        tools: Optional[List[dict]] = None,
-        tool_choice: Literal["none", "auto", "required"] = "auto",
-        **kwargs,
-    ) -> OpenAIResponse:
-        # Main entry point for chat completion
-        bedrock_tools = []
-        if tools is not None:
-            bedrock_tools = self._convert_openai_tools_to_bedrock_format(tools)
-        if stream:
-            return self._invoke_bedrock_stream(
-                model,
-                messages,
-                max_tokens,
-                temperature,
-                bedrock_tools,
-                tool_choice,
-                **kwargs,
-            )
-        else:
-            return self._invoke_bedrock(
-                model,
-                messages,
-                max_tokens,
-                temperature,
-                bedrock_tools,
-                tool_choice,
-                **kwargs,
-            )

app/config.py

@@ -37,18 +37,6 @@ class ProxySettings(BaseModel):
 
 class SearchSettings(BaseModel):
     engine: str = Field(default="Google", description="Search engine the llm to use")
-    fallback_engines: List[str] = Field(
-        default_factory=lambda: ["DuckDuckGo", "Baidu"],
-        description="Fallback search engines to try if the primary engine fails",
-    )
-    retry_delay: int = Field(
-        default=60,
-        description="Seconds to wait before retrying all engines again after they all fail",
-    )
-    max_retries: int = Field(
-        default=3,
-        description="Maximum number of times to retry all engines when all fail",
-    )
 
 
 class BrowserSettings(BaseModel):

app/flow/base.py

@@ -1,4 +1,5 @@
 from abc import ABC, abstractmethod
+from enum import Enum
 from typing import Dict, List, Optional, Union
 
 from pydantic import BaseModel
@@ -6,6 +7,10 @@ from pydantic import BaseModel
 from app.agent.base import BaseAgent
 
 
+class FlowType(str, Enum):
+    PLANNING = "planning"
+
+
 class BaseFlow(BaseModel, ABC):
     """Base class for execution flows supporting multiple agents"""
 
@@ -55,3 +60,32 @@ class BaseFlow(BaseModel, ABC):
     @abstractmethod
     async def execute(self, input_text: str) -> str:
         """Execute the flow with given input"""
+
+
+class PlanStepStatus(str, Enum):
+    """Enum class defining possible statuses of a plan step"""
+
+    NOT_STARTED = "not_started"
+    IN_PROGRESS = "in_progress"
+    COMPLETED = "completed"
+    BLOCKED = "blocked"
+
+    @classmethod
+    def get_all_statuses(cls) -> list[str]:
+        """Return a list of all possible step status values"""
+        return [status.value for status in cls]
+
+    @classmethod
+    def get_active_statuses(cls) -> list[str]:
+        """Return a list of values representing active statuses (not started or in progress)"""
+        return [cls.NOT_STARTED.value, cls.IN_PROGRESS.value]
+
+    @classmethod
+    def get_status_marks(cls) -> Dict[str, str]:
+        """Return a mapping of statuses to their marker symbols"""
+        return {
+            cls.COMPLETED.value: "[✓]",
+            cls.IN_PROGRESS.value: "[→]",
+            cls.BLOCKED.value: "[!]",
+            cls.NOT_STARTED.value: "[ ]",
+        }

app/flow/flow_factory.py

@@ -1,15 +1,10 @@
-from enum import Enum
 from typing import Dict, List, Union
 
 from app.agent.base import BaseAgent
-from app.flow.base import BaseFlow
+from app.flow.base import BaseFlow, FlowType
 from app.flow.planning import PlanningFlow
 
 
-class FlowType(str, Enum):
-    PLANNING = "planning"
-
-
 class FlowFactory:
     """Factory for creating different types of flows with support for multiple agents"""
 

app/flow/planning.py

@@ -1,47 +1,17 @@
 import json
 import time
-from enum import Enum
 from typing import Dict, List, Optional, Union
 
 from pydantic import Field
 
 from app.agent.base import BaseAgent
-from app.flow.base import BaseFlow
+from app.flow.base import BaseFlow, PlanStepStatus
 from app.llm import LLM
 from app.logger import logger
 from app.schema import AgentState, Message, ToolChoice
 from app.tool import PlanningTool
 
 
-class PlanStepStatus(str, Enum):
-    """Enum class defining possible statuses of a plan step"""
-
-    NOT_STARTED = "not_started"
-    IN_PROGRESS = "in_progress"
-    COMPLETED = "completed"
-    BLOCKED = "blocked"
-
-    @classmethod
-    def get_all_statuses(cls) -> list[str]:
-        """Return a list of all possible step status values"""
-        return [status.value for status in cls]
-
-    @classmethod
-    def get_active_statuses(cls) -> list[str]:
-        """Return a list of values representing active statuses (not started or in progress)"""
-        return [cls.NOT_STARTED.value, cls.IN_PROGRESS.value]
-
-    @classmethod
-    def get_status_marks(cls) -> Dict[str, str]:
-        """Return a mapping of statuses to their marker symbols"""
-        return {
-            cls.COMPLETED.value: "[✓]",
-            cls.IN_PROGRESS.value: "[→]",
-            cls.BLOCKED.value: "[!]",
-            cls.NOT_STARTED.value: "[ ]",
-        }
-
-
 class PlanningFlow(BaseFlow):
     """A flow that manages planning and execution of tasks using agents."""
 

app/llm.py

@@ -18,7 +18,6 @@ from tenacity import (
     wait_random_exponential,
 )
 
-from app.bedrock import BedrockClient
 from app.config import LLMSettings, config
 from app.exceptions import TokenLimitExceeded
 from app.logger import logger  # Assuming a logger is set up in your app
@@ -226,8 +225,6 @@ class LLM:
                     api_key=self.api_key,
                     api_version=self.api_version,
                 )
-            elif self.api_type == "aws":
-                self.client = BedrockClient()
             else:
                 self.client = AsyncOpenAI(api_key=self.api_key, base_url=self.base_url)
 

app/mcp/server.py

@@ -1,19 +1,30 @@
-import logging
-import sys
-
-
-logging.basicConfig(level=logging.INFO, handlers=[logging.StreamHandler(sys.stderr)])
-
 import argparse
 import asyncio
 import atexit
 import json
+import logging
+import os
+import sys
 from inspect import Parameter, Signature
 from typing import Any, Dict, Optional
 
 from mcp.server.fastmcp import FastMCP
 
-from app.logger import logger
+
+# Add directories to Python path (needed for proper importing)
+current_dir = os.path.dirname(os.path.abspath(__file__))
+parent_dir = os.path.dirname(current_dir)
+root_dir = os.path.dirname(parent_dir)
+sys.path.insert(0, parent_dir)
+sys.path.insert(0, current_dir)
+sys.path.insert(0, root_dir)
+
+# Configure logging (using the same format as original)
+logging.basicConfig(
+    level=logging.INFO, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s"
+)
+logger = logging.getLogger("mcp-server")
+
 from app.tool.base import BaseTool
 from app.tool.bash import Bash
 from app.tool.browser_use_tool import BrowserUseTool
@@ -34,6 +45,11 @@ class MCPServer:
         self.tools["editor"] = StrReplaceEditor()
         self.tools["terminate"] = Terminate()
 
+        from app.logger import logger as app_logger
+
+        global logger
+        logger = app_logger
+
     def register_tool(self, tool: BaseTool, method_name: Optional[str] = None) -> None:
         """Register a tool with parameter validation and documentation."""
         tool_name = method_name or tool.name

app/tool/web_search.py

@@ -4,7 +4,6 @@ from typing import List
 from tenacity import retry, stop_after_attempt, wait_exponential
 
 from app.config import config
-from app.logger import logger
 from app.tool.base import BaseTool
 from app.tool.search import (
     BaiduSearchEngine,
@@ -45,8 +44,6 @@ class WebSearch(BaseTool):
     async def execute(self, query: str, num_results: int = 10) -> List[str]:
         """
         Execute a Web search and return a list of URLs.
-        Tries engines in order based on configuration, falling back if an engine fails with errors.
-        If all engines fail, it will wait and retry up to the configured number of times.
 
         Args:
             query (str): The search query to submit to the search engine.
@@ -55,109 +52,37 @@ class WebSearch(BaseTool):
         Returns:
             List[str]: A list of URLs matching the search query.
         """
-        # Get retry settings from config
-        retry_delay = 60  # Default to 60 seconds
-        max_retries = 3  # Default to 3 retries
-
-        if config.search_config:
-            retry_delay = getattr(config.search_config, "retry_delay", 60)
-            max_retries = getattr(config.search_config, "max_retries", 3)
-
-        # Try searching with retries when all engines fail
-        for retry_count in range(
-            max_retries + 1
-        ):  # +1 because first try is not a retry
-            links = await self._try_all_engines(query, num_results)
-            if links:
-                return links
-
-            if retry_count < max_retries:
-                # All engines failed, wait and retry
-                logger.warning(
-                    f"All search engines failed. Waiting {retry_delay} seconds before retry {retry_count + 1}/{max_retries}..."
-                )
-                await asyncio.sleep(retry_delay)
-            else:
-                logger.error(
-                    f"All search engines failed after {max_retries} retries. Giving up."
-                )
-
-        return []
-
-    async def _try_all_engines(self, query: str, num_results: int) -> List[str]:
-        """
-        Try all search engines in the configured order.
-
-        Args:
-            query (str): The search query to submit to the search engine.
-            num_results (int): The number of search results to return.
-
-        Returns:
-            List[str]: A list of URLs matching the search query, or empty list if all engines fail.
-        """
         engine_order = self._get_engine_order()
-        failed_engines = []
-
         for engine_name in engine_order:
             engine = self._search_engine[engine_name]
             try:
-                logger.info(f"🔎 Attempting search with {engine_name.capitalize()}...")
                 links = await self._perform_search_with_engine(
                     engine, query, num_results
                 )
                 if links:
-                    if failed_engines:
-                        logger.info(
-                            f"Search successful with {engine_name.capitalize()} after trying: {', '.join(failed_engines)}"
-                        )
                     return links
             except Exception as e:
-                failed_engines.append(engine_name.capitalize())
+                print(f"Search engine '{engine_name}' failed with error: {e}")
-                is_rate_limit = "429" in str(e) or "Too Many Requests" in str(e)
-
-                if is_rate_limit:
-                    logger.warning(
-                        f"⚠️ {engine_name.capitalize()} search engine rate limit exceeded, trying next engine..."
-                    )
-                else:
-                    logger.warning(
-                        f"⚠️ {engine_name.capitalize()} search failed with error: {e}"
-                    )
-
-        if failed_engines:
-            logger.error(f"All search engines failed: {', '.join(failed_engines)}")
         return []
 
     def _get_engine_order(self) -> List[str]:
         """
         Determines the order in which to try search engines.
-        Preferred engine is first (based on configuration), followed by fallback engines,
+        Preferred engine is first (based on configuration), followed by the remaining engines.
-        and then the remaining engines.
 
         Returns:
             List[str]: Ordered list of search engine names.
         """
         preferred = "google"
-        fallbacks = []
+        if config.search_config and config.search_config.engine:
-
+            preferred = config.search_config.engine.lower()
-        if config.search_config:
-            if config.search_config.engine:
-                preferred = config.search_config.engine.lower()
-            if config.search_config.fallback_engines:
-                fallbacks = [
-                    engine.lower() for engine in config.search_config.fallback_engines
-                ]
 
         engine_order = []
-        # Add preferred engine first
         if preferred in self._search_engine:
             engine_order.append(preferred)
-
+        for key in self._search_engine:
-        # Add configured fallback engines in order
+            if key not in engine_order:
-        for fallback in fallbacks:
+                engine_order.append(key)
-            if fallback in self._search_engine and fallback not in engine_order:
-                engine_order.append(fallback)
-
         return engine_order
 
     @retry(

config/config.example.toml

@@ -6,14 +6,6 @@ api_key = "YOUR_API_KEY"                    # Your API key
 max_tokens = 8192                           # Maximum number of tokens in the response
 temperature = 0.0                           # Controls randomness
 
-# [llm] # Amazon Bedrock
-# api_type = "aws"                                       # Required
-# model = "us.anthropic.claude-3-7-sonnet-20250219-v1:0" # Bedrock supported modelID
-# base_url = "bedrock-runtime.us-west-2.amazonaws.com"   # Not used now
-# max_tokens = 8192
-# temperature = 1.0
-# api_key = "bear"                                       # Required but not used for Bedrock
-
 # [llm] #AZURE OPENAI:
 # api_type= 'azure'
 # model = "YOUR_MODEL_NAME" #"gpt-4o-mini"
@@ -73,13 +65,6 @@ temperature = 0.0                           # Controls randomness for vision mod
 # [search]
 # Search engine for agent to use. Default is "Google", can be set to "Baidu" or "DuckDuckGo".
 #engine = "Google"
-# Fallback engine order. Default is ["DuckDuckGo", "Baidu"] - will try in this order after primary engine fails.
-#fallback_engines = ["DuckDuckGo", "Baidu"]
-# Seconds to wait before retrying all engines again when they all fail due to rate limits. Default is 60.
-#retry_delay = 60
-# Maximum number of times to retry all engines when all fail. Default is 3.
-#max_retries = 3
-
 
 ## Sandbox configuration
 #[sandbox]

requirements.txt

@@ -31,5 +31,3 @@ pytest-asyncio~=0.25.3
 mcp~=1.4.1
 httpx>=0.27.0
 tomli>=2.0.0
-
-boto3~=1.37.16

run_mcp.py

@@ -13,14 +13,10 @@ class MCPRunner:
 
     def __init__(self):
         self.root_path = config.root_path
-        self.server_reference = "app.mcp.server"
+        self.server_script = self.root_path / "app" / "mcp" / "server.py"
         self.agent = MCPAgent()
 
-    async def initialize(
+    async def initialize(self, connection_type: str, server_url: str = None) -> None:
-        self,
-        connection_type: str,
-        server_url: str | None = None,
-    ) -> None:
         """Initialize the MCP agent with the appropriate connection."""
         logger.info(f"Initializing MCPAgent with {connection_type} connection...")
 
@@ -28,7 +24,7 @@ class MCPRunner:
             await self.agent.initialize(
                 connection_type="stdio",
                 command=sys.executable,
-                args=["-m", self.server_reference],
+                args=[str(self.server_script)],
             )
         else:  # sse
             await self.agent.initialize(connection_type="sse", server_url=server_url)
@@ -51,14 +47,9 @@ class MCPRunner:
 
     async def run_default(self) -> None:
         """Run the agent in default mode."""
-        prompt = input("Enter your prompt: ")
+        await self.agent.run(
-        if not prompt.strip():
+            "Hello, what tools are available to me? Terminate after you have listed the tools."
-            logger.warning("Empty prompt provided.")
+        )
-            return
-
-        logger.warning("Processing your request...")
-        await self.agent.run(prompt)
-        logger.info("Request processing completed.")
 
     async def cleanup(self) -> None:
         """Clean up agent resources."""

run_mcp_server.py

@@ -1,11 +0,0 @@
-# coding: utf-8
-# A shortcut to launch OpenManus MCP server, where its introduction also solves other import issues.
-from app.mcp.server import MCPServer, parse_args
-
-
-if __name__ == "__main__":
-    args = parse_args()
-
-    # Create and run server (maintaining original flow)
-    server = MCPServer()
-    server.run(transport=args.transport)