Add files using upload-large-folder tool

projects/ui/DeepCode/.github/ISSUE_TEMPLATE/bug_report.yml

@@ -0,0 +1,61 @@
+name: Bug Report
+description: File a bug report
+title: "[Bug]:"
+labels: ["bug", "triage"]
+
+body:
+  - type: checkboxes
+    id: existingcheck
+    attributes:
+      label: Do you need to file an issue?
+      description: Please help us manage our time by avoiding duplicates and common bugs with the steps below.
+      options:
+        - label: I have searched the existing issues and this bug is not already filed.
+        - label: I believe this is a legitimate bug, not just a question or feature request.
+  - type: textarea
+    id: description
+    attributes:
+      label: Describe the bug
+      description: A clear and concise description of what the bug is.
+      placeholder: What went wrong?
+  - type: textarea
+    id: reproduce
+    attributes:
+      label: Steps to reproduce
+      description: Steps to reproduce the behavior.
+      placeholder: How can we replicate the issue?
+  - type: textarea
+    id: expected_behavior
+    attributes:
+      label: Expected Behavior
+      description: A clear and concise description of what you expected to happen.
+      placeholder: What should have happened?
+  - type: textarea
+    id: configused
+    attributes:
+      label: DeepCode Config Used
+      description: The DeepCode configuration used for the run.
+      placeholder: The settings content or DeepCode configuration
+      value: |
+        # Paste your config here
+  - type: textarea
+    id: screenshotslogs
+    attributes:
+      label: Logs and screenshots
+      description: If applicable, add screenshots and logs to help explain your problem.
+      placeholder: Add logs and screenshots here
+  - type: textarea
+    id: additional_information
+    attributes:
+      label: Additional Information
+      description: |
+        - DeepCode Version: e.g., v0.1.1
+        - Operating System: e.g., Windows 10, Ubuntu 20.04
+        - Python Version: e.g., 3.8
+        - Related Issues: e.g., #1
+        - Any other relevant information.
+      value: |
+        - DeepCode Version:
+        - Operating System:
+        - Python Version:
+        - Related Issues:

projects/ui/DeepCode/.github/ISSUE_TEMPLATE/config.yml

@@ -0,0 +1 @@
+blank_issues_enabled: false

projects/ui/DeepCode/.github/ISSUE_TEMPLATE/feature_request.yml

@@ -0,0 +1,26 @@
+name: Feature Request
+description: File a feature request
+labels: ["enhancement"]
+title: "[Feature Request]:"
+
+body:
+  - type: checkboxes
+    id: existingcheck
+    attributes:
+      label: Do you need to file a feature request?
+      description: Please help us manage our time by avoiding duplicates and common feature request with the steps below.
+      options:
+        - label: I have searched the existing feature request and this feature request is not already filed.
+        - label: I believe this is a legitimate feature request, not just a question or bug.
+  - type: textarea
+    id: feature_request_description
+    attributes:
+      label: Feature Request Description
+      description: A clear and concise description of the feature request you would like.
+      placeholder: What this feature request add more or improve?
+  - type: textarea
+    id: additional_context
+    attributes:
+      label: Additional Context
+      description: Add any other context or screenshots about the feature request here.
+      placeholder: Any additional information

projects/ui/DeepCode/.github/ISSUE_TEMPLATE/question.yml

@@ -0,0 +1,26 @@
+name: Question
+description: Ask a general question
+labels: ["question"]
+title: "[Question]:"
+
+body:
+  - type: checkboxes
+    id: existingcheck
+    attributes:
+      label: Do you need to ask a question?
+      description: Please help us manage our time by avoiding duplicates and common questions with the steps below.
+      options:
+        - label: I have searched the existing question and discussions and this question is not already answered.
+        - label: I believe this is a legitimate question, not just a bug or feature request.
+  - type: textarea
+    id: question
+    attributes:
+      label: Your Question
+      description: A clear and concise description of your question.
+      placeholder: What is your question?
+  - type: textarea
+    id: context
+    attributes:
+      label: Additional Context
+      description: Provide any additional context or details that might help us understand your question better.
+      placeholder: Add any relevant information here

projects/ui/DeepCode/.github/workflows/linting.yaml

@@ -0,0 +1,30 @@
+name: Linting and Formatting
+
+on:
+    push:
+        branches:
+            - main
+    pull_request:
+        branches:
+            - main
+
+jobs:
+    lint-and-format:
+        runs-on: ubuntu-latest
+
+        steps:
+            - name: Checkout code
+              uses: actions/checkout@v2
+
+            - name: Set up Python
+              uses: actions/setup-python@v2
+              with:
+                python-version: '3.x'
+
+            - name: Install dependencies
+              run: |
+                python -m pip install --upgrade pip
+                pip install pre-commit
+
+            - name: Run pre-commit
+              run: pre-commit run --all-files --show-diff-on-failure

projects/ui/DeepCode/.github/workflows/pypi-publish.yml

@@ -0,0 +1,52 @@
+name: Upload DeepCode Package
+
+on:
+  release:
+    types: [published]
+
+permissions:
+  contents: read
+
+jobs:
+  release-build:
+    runs-on: ubuntu-latest
+
+    steps:
+      - uses: actions/checkout@v4
+
+      - uses: actions/setup-python@v5
+        with:
+          python-version: "3.x"
+
+      - name: Build release distributions
+        run: |
+          python -m pip install build
+          python -m build
+
+      - name: Upload distributions
+        uses: actions/upload-artifact@v4
+        with:
+          name: release-dists
+          path: dist/
+
+  pypi-publish:
+    runs-on: ubuntu-latest
+    needs:
+      - release-build
+    permissions:
+      id-token: write
+
+    environment:
+      name: pypi
+
+    steps:
+      - name: Retrieve release distributions
+        uses: actions/download-artifact@v4
+        with:
+          name: release-dists
+          path: dist/
+
+      - name: Publish release distributions to PyPI
+        uses: pypa/gh-action-pypi-publish@release/v1
+        with:
+          packages-dir: dist/

projects/ui/DeepCode/cli/workflows/__init__.py

@@ -0,0 +1,11 @@
+"""
+CLI-specific Workflow Adapters
+CLI专用工作流适配器
+
+This module provides CLI-optimized versions of workflow components that are
+specifically adapted for command-line interface usage patterns.
+"""
+
+from .cli_workflow_adapter import CLIWorkflowAdapter
+
+__all__ = ["CLIWorkflowAdapter"]

projects/ui/DeepCode/cli/workflows/cli_workflow_adapter.py

@@ -0,0 +1,336 @@
+"""
+CLI Workflow Adapter for Agent Orchestration Engine
+CLI工作流适配器 - 智能体编排引擎
+
+This adapter provides CLI-optimized interface to the latest agent orchestration engine,
+with enhanced progress reporting, error handling, and CLI-specific optimizations.
+"""
+
+import os
+from typing import Callable, Dict, Any
+from mcp_agent.app import MCPApp
+
+
+class CLIWorkflowAdapter:
+    """
+    CLI-optimized workflow adapter for the intelligent agent orchestration engine.
+
+    This adapter provides:
+    - Enhanced CLI progress reporting
+    - Optimized error handling for CLI environments
+    - Streamlined interface for command-line usage
+    - Integration with the latest agent orchestration engine
+    """
+
+    def __init__(self, cli_interface=None):
+        """
+        Initialize CLI workflow adapter.
+
+        Args:
+            cli_interface: CLI interface instance for progress reporting
+        """
+        self.cli_interface = cli_interface
+        self.app = None
+        self.logger = None
+        self.context = None
+
+    async def initialize_mcp_app(self) -> Dict[str, Any]:
+        """
+        Initialize MCP application for CLI usage.
+
+        Returns:
+            dict: Initialization result
+        """
+        try:
+            if self.cli_interface:
+                self.cli_interface.show_spinner(
+                    "🚀 Initializing Agent Orchestration Engine", 2.0
+                )
+
+            # Initialize MCP application
+            self.app = MCPApp(name="cli_agent_orchestration")
+            self.app_context = self.app.run()
+            agent_app = await self.app_context.__aenter__()
+
+            self.logger = agent_app.logger
+            self.context = agent_app.context
+
+            # Configure filesystem access
+            import os
+
+            self.context.config.mcp.servers["filesystem"].args.extend([os.getcwd()])
+
+            if self.cli_interface:
+                self.cli_interface.print_status(
+                    "🧠 Agent Orchestration Engine initialized successfully", "success"
+                )
+
+            return {
+                "status": "success",
+                "message": "MCP application initialized successfully",
+            }
+
+        except Exception as e:
+            error_msg = f"Failed to initialize MCP application: {str(e)}"
+            if self.cli_interface:
+                self.cli_interface.print_status(error_msg, "error")
+            return {"status": "error", "message": error_msg}
+
+    async def cleanup_mcp_app(self):
+        """
+        Clean up MCP application resources.
+        """
+        if hasattr(self, "app_context"):
+            try:
+                await self.app_context.__aexit__(None, None, None)
+                if self.cli_interface:
+                    self.cli_interface.print_status(
+                        "🧹 Resources cleaned up successfully", "info"
+                    )
+            except Exception as e:
+                if self.cli_interface:
+                    self.cli_interface.print_status(
+                        f"⚠️ Cleanup warning: {str(e)}", "warning"
+                    )
+
+    def create_cli_progress_callback(self) -> Callable:
+        """
+        Create CLI-optimized progress callback function.
+
+        Returns:
+            Callable: Progress callback function
+        """
+
+        def progress_callback(progress: int, message: str):
+            if self.cli_interface:
+                # Map progress to CLI stages
+                if progress <= 10:
+                    self.cli_interface.display_processing_stages(1)
+                elif progress <= 25:
+                    self.cli_interface.display_processing_stages(2)
+                elif progress <= 40:
+                    self.cli_interface.display_processing_stages(3)
+                elif progress <= 50:
+                    self.cli_interface.display_processing_stages(4)
+                elif progress <= 60:
+                    self.cli_interface.display_processing_stages(5)
+                elif progress <= 70:
+                    self.cli_interface.display_processing_stages(6)
+                elif progress <= 85:
+                    self.cli_interface.display_processing_stages(7)
+                else:
+                    self.cli_interface.display_processing_stages(8)
+
+                # Display status message
+                self.cli_interface.print_status(message, "processing")
+
+        return progress_callback
+
+    async def execute_full_pipeline(
+        self, input_source: str, enable_indexing: bool = True
+    ) -> Dict[str, Any]:
+        """
+        Execute the complete intelligent multi-agent research orchestration pipeline.
+
+        Args:
+            input_source: Research input source (file path, URL, or preprocessed analysis)
+            enable_indexing: Whether to enable advanced intelligence analysis
+
+        Returns:
+            dict: Comprehensive pipeline execution result
+        """
+        try:
+            # Import the latest agent orchestration engine
+            from workflows.agent_orchestration_engine import (
+                execute_multi_agent_research_pipeline,
+            )
+
+            # Create CLI progress callback
+            progress_callback = self.create_cli_progress_callback()
+
+            # Display pipeline start
+            if self.cli_interface:
+                mode = "comprehensive" if enable_indexing else "optimized"
+                self.cli_interface.print_status(
+                    f"🚀 Starting {mode} agent orchestration pipeline...", "processing"
+                )
+                self.cli_interface.display_processing_stages(0)
+
+            # Execute the pipeline
+            result = await execute_multi_agent_research_pipeline(
+                input_source=input_source,
+                logger=self.logger,
+                progress_callback=progress_callback,
+                enable_indexing=enable_indexing,
+            )
+
+            # Display completion
+            if self.cli_interface:
+                self.cli_interface.display_processing_stages(8)
+                self.cli_interface.print_status(
+                    "🎉 Agent orchestration pipeline completed successfully!",
+                    "complete",
+                )
+
+            return {
+                "status": "success",
+                "result": result,
+                "pipeline_mode": "comprehensive" if enable_indexing else "optimized",
+            }
+
+        except Exception as e:
+            error_msg = f"Pipeline execution failed: {str(e)}"
+            if self.cli_interface:
+                self.cli_interface.print_status(error_msg, "error")
+
+            return {
+                "status": "error",
+                "error": error_msg,
+                "pipeline_mode": "comprehensive" if enable_indexing else "optimized",
+            }
+
+    async def execute_chat_pipeline(self, user_input: str) -> Dict[str, Any]:
+        """
+        Execute the chat-based planning and implementation pipeline.
+
+        Args:
+            user_input: User's coding requirements and description
+
+        Returns:
+            dict: Chat pipeline execution result
+        """
+        try:
+            # Import the chat-based pipeline
+            from workflows.agent_orchestration_engine import (
+                execute_chat_based_planning_pipeline,
+            )
+
+            # Create CLI progress callback for chat mode
+            def chat_progress_callback(progress: int, message: str):
+                if self.cli_interface:
+                    # Map progress to CLI stages for chat mode
+                    if progress <= 5:
+                        self.cli_interface.display_processing_stages(
+                            0, chat_mode=True
+                        )  # Initialize
+                    elif progress <= 30:
+                        self.cli_interface.display_processing_stages(
+                            1, chat_mode=True
+                        )  # Planning
+                    elif progress <= 50:
+                        self.cli_interface.display_processing_stages(
+                            2, chat_mode=True
+                        )  # Setup
+                    elif progress <= 70:
+                        self.cli_interface.display_processing_stages(
+                            3, chat_mode=True
+                        )  # Save Plan
+                    else:
+                        self.cli_interface.display_processing_stages(
+                            4, chat_mode=True
+                        )  # Implement
+
+                    # Display status message
+                    self.cli_interface.print_status(message, "processing")
+
+            # Display pipeline start
+            if self.cli_interface:
+                self.cli_interface.print_status(
+                    "🚀 Starting chat-based planning pipeline...", "processing"
+                )
+                self.cli_interface.display_processing_stages(0, chat_mode=True)
+
+            # Execute the chat pipeline with indexing enabled for enhanced code understanding
+            result = await execute_chat_based_planning_pipeline(
+                user_input=user_input,
+                logger=self.logger,
+                progress_callback=chat_progress_callback,
+                enable_indexing=True,  # Enable indexing for better code implementation
+            )
+
+            # Display completion
+            if self.cli_interface:
+                self.cli_interface.display_processing_stages(
+                    4, chat_mode=True
+                )  # Final stage for chat mode
+                self.cli_interface.print_status(
+                    "🎉 Chat-based planning pipeline completed successfully!",
+                    "complete",
+                )
+
+            return {"status": "success", "result": result, "pipeline_mode": "chat"}
+
+        except Exception as e:
+            error_msg = f"Chat pipeline execution failed: {str(e)}"
+            if self.cli_interface:
+                self.cli_interface.print_status(error_msg, "error")
+
+            return {"status": "error", "error": error_msg, "pipeline_mode": "chat"}
+
+    async def process_input_with_orchestration(
+        self, input_source: str, input_type: str, enable_indexing: bool = True
+    ) -> Dict[str, Any]:
+        """
+        Process input using the intelligent agent orchestration engine.
+
+        This is the main CLI interface to the latest agent orchestration capabilities.
+
+        Args:
+            input_source: Input source (file path or URL)
+            input_type: Type of input ('file' or 'url')
+            enable_indexing: Whether to enable advanced intelligence analysis
+
+        Returns:
+            dict: Processing result with status and details
+        """
+        pipeline_result = None
+
+        try:
+            # Initialize MCP app
+            init_result = await self.initialize_mcp_app()
+            if init_result["status"] != "success":
+                return init_result
+
+            # Process file:// URLs for traditional file/URL inputs
+            if input_source.startswith("file://"):
+                file_path = input_source[7:]
+                if os.name == "nt" and file_path.startswith("/"):
+                    file_path = file_path.lstrip("/")
+                input_source = file_path
+
+            # Execute appropriate pipeline based on input type
+            if input_type == "chat":
+                # Use chat-based planning pipeline for user requirements
+                pipeline_result = await self.execute_chat_pipeline(input_source)
+            else:
+                # Use traditional multi-agent research pipeline for files/URLs
+                pipeline_result = await self.execute_full_pipeline(
+                    input_source, enable_indexing=enable_indexing
+                )
+
+            return {
+                "status": pipeline_result["status"],
+                "analysis_result": "Integrated into agent orchestration pipeline",
+                "download_result": "Integrated into agent orchestration pipeline",
+                "repo_result": pipeline_result.get("result", ""),
+                "pipeline_mode": pipeline_result.get("pipeline_mode", "comprehensive"),
+                "error": pipeline_result.get("error"),
+            }
+
+        except Exception as e:
+            error_msg = f"Error during orchestrated processing: {str(e)}"
+            if self.cli_interface:
+                self.cli_interface.print_status(error_msg, "error")
+
+            return {
+                "status": "error",
+                "error": error_msg,
+                "analysis_result": "",
+                "download_result": "",
+                "repo_result": "",
+                "pipeline_mode": "comprehensive" if enable_indexing else "optimized",
+            }
+
+        finally:
+            # Clean up resources
+            await self.cleanup_mcp_app()

projects/ui/DeepCode/workflows/agents/__init__.py

@@ -0,0 +1,12 @@
+"""
+Agents Package for Code Implementation Workflow
+
+This package contains specialized agents for different aspects of code implementation:
+- CodeImplementationAgent: Handles file-by-file code generation
+- ConciseMemoryAgent: Manages memory optimization and consistency across phases
+"""
+
+from .code_implementation_agent import CodeImplementationAgent
+from .memory_agent_concise import ConciseMemoryAgent as MemoryAgent
+
+__all__ = ["CodeImplementationAgent", "MemoryAgent"]

projects/ui/DeepCode/workflows/agents/code_implementation_agent.py

@@ -0,0 +1,1118 @@
+"""
+Code Implementation Agent for File-by-File Development
+
+Handles systematic code implementation with progress tracking and
+memory optimization for long-running development sessions.
+"""
+
+import json
+import time
+import logging
+from typing import Dict, Any, List, Optional
+
+# Import tiktoken for token calculation
+try:
+    import tiktoken
+
+    TIKTOKEN_AVAILABLE = True
+except ImportError:
+    TIKTOKEN_AVAILABLE = False
+
+# Import prompts from code_prompts
+import sys
+import os
+
+sys.path.insert(
+    0, os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+)
+from prompts.code_prompts import (
+    GENERAL_CODE_IMPLEMENTATION_SYSTEM_PROMPT,
+)
+
+
+class CodeImplementationAgent:
+    """
+    Code Implementation Agent for systematic file-by-file development
+
+    Responsibilities:
+    - Track file implementation progress
+    - Execute MCP tool calls for code generation
+    - Monitor implementation status
+    - Coordinate with Summary Agent for memory optimization
+    - Calculate token usage for context management
+    """
+
+    def __init__(
+        self,
+        mcp_agent,
+        logger: Optional[logging.Logger] = None,
+        enable_read_tools: bool = True,
+    ):
+        """
+        Initialize Code Implementation Agent
+
+        Args:
+            mcp_agent: MCP agent instance for tool calls
+            logger: Logger instance for tracking operations
+            enable_read_tools: Whether to enable read_file and read_code_mem tools (default: True)
+        """
+        self.mcp_agent = mcp_agent
+        self.logger = logger or self._create_default_logger()
+        self.enable_read_tools = enable_read_tools  # Control read tools execution
+
+        self.implementation_summary = {
+            "completed_files": [],
+            "technical_decisions": [],
+            "important_constraints": [],
+            "architecture_notes": [],
+            "dependency_analysis": [],  # Track dependency analysis and file reads
+        }
+        self.files_implemented_count = 0
+        self.implemented_files_set = (
+            set()
+        )  # Track unique file paths to avoid duplicate counting
+        self.files_read_for_dependencies = (
+            set()
+        )  # Track files read for dependency analysis
+        self.last_summary_file_count = (
+            0  # Track the file count when last summary was triggered
+        )
+
+        # Token calculation settings
+        self.max_context_tokens = (
+            200000  # Default max context tokens for Claude-3.5-Sonnet
+        )
+        self.token_buffer = 10000  # Safety buffer before reaching max
+        self.summary_trigger_tokens = (
+            self.max_context_tokens - self.token_buffer
+        )  # Trigger summary when approaching limit
+        self.last_summary_token_count = (
+            0  # Track token count when last summary was triggered
+        )
+
+        # Initialize tokenizer
+        if TIKTOKEN_AVAILABLE:
+            try:
+                # Use Claude-3 tokenizer (approximation with OpenAI's o200k_base)
+                self.tokenizer = tiktoken.get_encoding("o200k_base")
+                self.logger.info("Token calculation enabled with o200k_base encoding")
+            except Exception as e:
+                self.tokenizer = None
+                self.logger.warning(f"Failed to initialize tokenizer: {e}")
+        else:
+            self.tokenizer = None
+            self.logger.warning(
+                "tiktoken not available, token-based summary triggering disabled"
+            )
+
+        # Analysis loop detection
+        self.recent_tool_calls = []  # Track recent tool calls to detect analysis loops
+        self.max_read_without_write = 5  # Max read_file calls without write_file
+
+        # Memory agent integration
+        self.memory_agent = None  # Will be set externally
+        self.llm_client = None  # Will be set externally
+        self.llm_client_type = None  # Will be set externally
+
+        # Log read tools configuration
+        read_tools_status = "ENABLED" if self.enable_read_tools else "DISABLED"
+        self.logger.info(
+            f"🔧 Code Implementation Agent initialized - Read tools: {read_tools_status}"
+        )
+        if not self.enable_read_tools:
+            self.logger.info(
+                "🚫 Testing mode: read_file and read_code_mem will be skipped when called"
+            )
+
+    def _create_default_logger(self) -> logging.Logger:
+        """Create default logger if none provided"""
+        logger = logging.getLogger(f"{__name__}.CodeImplementationAgent")
+        # Don't add handlers to child loggers - let them propagate to root
+        logger.setLevel(logging.INFO)
+        return logger
+
+    def get_system_prompt(self) -> str:
+        """
+        Get the system prompt for code implementation
+        """
+        return GENERAL_CODE_IMPLEMENTATION_SYSTEM_PROMPT
+
+    def set_memory_agent(self, memory_agent, llm_client=None, llm_client_type=None):
+        """
+        Set memory agent for code summary generation
+
+        Args:
+            memory_agent: Memory agent instance
+            llm_client: LLM client for summary generation
+            llm_client_type: Type of LLM client ("anthropic" or "openai")
+        """
+        self.memory_agent = memory_agent
+        self.llm_client = llm_client
+        self.llm_client_type = llm_client_type
+        self.logger.info("Memory agent integration configured")
+
+    async def execute_tool_calls(self, tool_calls: List[Dict]) -> List[Dict]:
+        """
+        Execute MCP tool calls and track implementation progress
+
+        Args:
+            tool_calls: List of tool calls to execute
+
+        Returns:
+            List of tool execution results
+        """
+        results = []
+
+        for tool_call in tool_calls:
+            tool_name = tool_call["name"]
+            tool_input = tool_call["input"]
+
+            self.logger.info(f"Executing MCP tool: {tool_name}")
+
+            try:
+                # Check if read tools are disabled
+                if not self.enable_read_tools and tool_name in [
+                    "read_file",
+                    "read_code_mem",
+                ]:
+                    # self.logger.info(f"🚫 SKIPPING {tool_name} - Read tools disabled for testing")
+                    # Return a mock result indicating the tool was skipped
+                    mock_result = json.dumps(
+                        {
+                            "status": "skipped",
+                            "message": f"{tool_name} tool disabled for testing",
+                            "tool_disabled": True,
+                            "original_input": tool_input,
+                        },
+                        ensure_ascii=False,
+                    )
+
+                    results.append(
+                        {
+                            "tool_id": tool_call["id"],
+                            "tool_name": tool_name,
+                            "result": mock_result,
+                        }
+                    )
+                    continue
+
+                # read_code_mem is now a proper MCP tool, no special handling needed
+
+                # INTERCEPT read_file calls - redirect to read_code_mem first if memory agent is available
+                if tool_name == "read_file":
+                    file_path = tool_call["input"].get("file_path", "unknown")
+                    self.logger.info(f"🔍 READ_FILE CALL DETECTED: {file_path}")
+                    self.logger.info(
+                        f"📊 Files implemented count: {self.files_implemented_count}"
+                    )
+                    self.logger.info(
+                        f"🧠 Memory agent available: {self.memory_agent is not None}"
+                    )
+
+                    # Enable optimization if memory agent is available (more aggressive approach)
+                    if self.memory_agent is not None:
+                        self.logger.info(
+                            f"🔄 INTERCEPTING read_file call for {file_path} (memory agent available)"
+                        )
+                        result = await self._handle_read_file_with_memory_optimization(
+                            tool_call
+                        )
+                        results.append(result)
+                        continue
+                    else:
+                        self.logger.info(
+                            "📁 NO INTERCEPTION: no memory agent available"
+                        )
+
+                if self.mcp_agent:
+                    # Execute tool call through MCP protocol
+                    result = await self.mcp_agent.call_tool(tool_name, tool_input)
+
+                    # Track file implementation progress
+                    if tool_name == "write_file":
+                        await self._track_file_implementation_with_summary(
+                            tool_call, result
+                        )
+                    elif tool_name == "read_file":
+                        self._track_dependency_analysis(tool_call, result)
+
+                    # Track tool calls for analysis loop detection
+                    self._track_tool_call_for_loop_detection(tool_name)
+
+                    results.append(
+                        {
+                            "tool_id": tool_call["id"],
+                            "tool_name": tool_name,
+                            "result": result,
+                        }
+                    )
+                else:
+                    results.append(
+                        {
+                            "tool_id": tool_call["id"],
+                            "tool_name": tool_name,
+                            "result": json.dumps(
+                                {
+                                    "status": "error",
+                                    "message": "MCP agent not initialized",
+                                },
+                                ensure_ascii=False,
+                            ),
+                        }
+                    )
+
+            except Exception as e:
+                self.logger.error(f"MCP tool execution failed: {e}")
+                results.append(
+                    {
+                        "tool_id": tool_call["id"],
+                        "tool_name": tool_name,
+                        "result": json.dumps(
+                            {"status": "error", "message": str(e)}, ensure_ascii=False
+                        ),
+                    }
+                )
+
+        return results
+
+    # _handle_read_code_mem method removed - read_code_mem is now a proper MCP tool
+
+    async def _handle_read_file_with_memory_optimization(self, tool_call: Dict) -> Dict:
+        """
+        Intercept read_file calls and redirect to read_code_mem if a summary exists.
+        This prevents unnecessary file reads if the summary is already available.
+        """
+        file_path = tool_call["input"].get("file_path")
+        if not file_path:
+            return {
+                "tool_id": tool_call["id"],
+                "tool_name": "read_file",
+                "result": json.dumps(
+                    {"status": "error", "message": "file_path parameter is required"},
+                    ensure_ascii=False,
+                ),
+            }
+
+        # Check if a summary exists for this file using read_code_mem MCP tool
+        should_use_summary = False
+        if self.memory_agent and self.mcp_agent:
+            try:
+                # Use read_code_mem MCP tool to check if summary exists (pass file path as list)
+                read_code_mem_result = await self.mcp_agent.call_tool(
+                    "read_code_mem", {"file_paths": [file_path]}
+                )
+
+                # Parse the result to check if summary was found
+                import json
+
+                if isinstance(read_code_mem_result, str):
+                    try:
+                        result_data = json.loads(read_code_mem_result)
+                        # Check if any summaries were found in the results
+                        should_use_summary = (
+                            result_data.get("status")
+                            in ["all_summaries_found", "partial_summaries_found"]
+                            and result_data.get("summaries_found", 0) > 0
+                        )
+                    except json.JSONDecodeError:
+                        should_use_summary = False
+            except Exception as e:
+                self.logger.debug(f"read_code_mem check failed for {file_path}: {e}")
+                should_use_summary = False
+
+        if should_use_summary:
+            self.logger.info(f"🔄 READ_FILE INTERCEPTED: Using summary for {file_path}")
+
+            # Use the MCP agent to call read_code_mem tool
+            if self.mcp_agent:
+                result = await self.mcp_agent.call_tool(
+                    "read_code_mem", {"file_paths": [file_path]}
+                )
+
+                # Modify the result to indicate it was originally a read_file call
+                import json
+
+                try:
+                    result_data = (
+                        json.loads(result) if isinstance(result, str) else result
+                    )
+                    if isinstance(result_data, dict):
+                        # Extract the specific file result for the single file we requested
+                        file_results = result_data.get("results", [])
+                        if file_results and len(file_results) > 0:
+                            specific_result = file_results[
+                                0
+                            ]  # Get the first (and only) result
+                            # Transform to match the old single-file format for backward compatibility
+                            transformed_result = {
+                                "status": specific_result.get("status", "no_summary"),
+                                "file_path": specific_result.get(
+                                    "file_path", file_path
+                                ),
+                                "summary_content": specific_result.get(
+                                    "summary_content"
+                                ),
+                                "message": specific_result.get("message", ""),
+                                "original_tool": "read_file",
+                                "optimization": "redirected_to_read_code_mem",
+                            }
+                            final_result = json.dumps(
+                                transformed_result, ensure_ascii=False
+                            )
+                        else:
+                            # Fallback if no results
+                            result_data["original_tool"] = "read_file"
+                            result_data["optimization"] = "redirected_to_read_code_mem"
+                            final_result = json.dumps(result_data, ensure_ascii=False)
+                    else:
+                        final_result = result
+                except (json.JSONDecodeError, TypeError):
+                    final_result = result
+
+                return {
+                    "tool_id": tool_call["id"],
+                    "tool_name": "read_file",  # Keep original tool name for tracking
+                    "result": final_result,
+                }
+            else:
+                self.logger.warning(
+                    "MCP agent not available for read_code_mem optimization"
+                )
+        else:
+            self.logger.info(
+                f"📁 READ_FILE: No summary for {file_path}, using actual file"
+            )
+
+            # Execute the original read_file call
+            if self.mcp_agent:
+                result = await self.mcp_agent.call_tool("read_file", tool_call["input"])
+
+                # Track dependency analysis for the actual file read
+                self._track_dependency_analysis(tool_call, result)
+
+                # Track tool calls for analysis loop detection
+                self._track_tool_call_for_loop_detection("read_file")
+
+                return {
+                    "tool_id": tool_call["id"],
+                    "tool_name": "read_file",
+                    "result": result,
+                }
+            else:
+                return {
+                    "tool_id": tool_call["id"],
+                    "tool_name": "read_file",
+                    "result": json.dumps(
+                        {"status": "error", "message": "MCP agent not initialized"},
+                        ensure_ascii=False,
+                    ),
+                }
+
+    async def _track_file_implementation_with_summary(
+        self, tool_call: Dict, result: Any
+    ):
+        """
+        Track file implementation and create code summary
+
+        Args:
+            tool_call: The write_file tool call
+            result: Result of the tool execution
+        """
+        # First do the regular tracking
+        self._track_file_implementation(tool_call, result)
+
+        # Then create and save code summary if memory agent is available
+        if self.memory_agent and self.llm_client and self.llm_client_type:
+            try:
+                file_path = tool_call["input"].get("file_path")
+                file_content = tool_call["input"].get("content", "")
+
+                if file_path and file_content:
+                    # Create code implementation summary
+                    summary = await self.memory_agent.create_code_implementation_summary(
+                        self.llm_client,
+                        self.llm_client_type,
+                        file_path,
+                        file_content,
+                        self.get_files_implemented_count(),  # Pass the current file count
+                    )
+
+                    self.logger.info(
+                        f"Created code summary for implemented file: {file_path}, summary: {summary[:100]}..."
+                    )
+                else:
+                    self.logger.warning(
+                        "Missing file path or content for summary generation"
+                    )
+
+            except Exception as e:
+                self.logger.error(f"Failed to create code summary: {e}")
+
+    def _track_file_implementation(self, tool_call: Dict, result: Any):
+        """
+        Track file implementation progress
+        """
+        try:
+            # Handle different result types from MCP
+            result_data = None
+
+            # Check if result is a CallToolResult object
+            if hasattr(result, "content"):
+                # Extract content from CallToolResult
+                if hasattr(result.content, "text"):
+                    result_content = result.content.text
+                else:
+                    result_content = str(result.content)
+
+                # Try to parse as JSON
+                try:
+                    result_data = json.loads(result_content)
+                except json.JSONDecodeError:
+                    # If not JSON, create a structure
+                    result_data = {
+                        "status": "success",
+                        "file_path": tool_call["input"].get("file_path", "unknown"),
+                    }
+            elif isinstance(result, str):
+                # Try to parse string result
+                try:
+                    result_data = json.loads(result)
+                except json.JSONDecodeError:
+                    result_data = {
+                        "status": "success",
+                        "file_path": tool_call["input"].get("file_path", "unknown"),
+                    }
+            elif isinstance(result, dict):
+                # Direct dictionary result
+                result_data = result
+            else:
+                # Fallback: assume success and extract file path from input
+                result_data = {
+                    "status": "success",
+                    "file_path": tool_call["input"].get("file_path", "unknown"),
+                }
+
+            # Extract file path for tracking
+            file_path = None
+            if result_data and result_data.get("status") == "success":
+                file_path = result_data.get(
+                    "file_path", tool_call["input"].get("file_path", "unknown")
+                )
+            else:
+                file_path = tool_call["input"].get("file_path")
+
+            # Only count unique files, not repeated tool calls on same file
+            if file_path and file_path not in self.implemented_files_set:
+                # This is a new file implementation
+                self.implemented_files_set.add(file_path)
+                self.files_implemented_count += 1
+                # self.logger.info(f"New file implementation tracked: count={self.files_implemented_count}, file={file_path}")
+                # print(f"New file implementation tracked: count={self.files_implemented_count}, file={file_path}")
+
+                # Add to completed files list
+                self.implementation_summary["completed_files"].append(
+                    {
+                        "file": file_path,
+                        "iteration": self.files_implemented_count,
+                        "timestamp": time.time(),
+                        "size": result_data.get("size", 0) if result_data else 0,
+                    }
+                )
+
+                # self.logger.info(
+                #     f"New file implementation tracked: count={self.files_implemented_count}, file={file_path}"
+                # )
+                # print(f"📝 NEW FILE IMPLEMENTED: count={self.files_implemented_count}, file={file_path}")
+                # print(f"🔧 OPTIMIZATION NOW ENABLED: files_implemented_count > 0 = {self.files_implemented_count > 0}")
+            elif file_path and file_path in self.implemented_files_set:
+                # This file was already implemented (duplicate tool call)
+                self.logger.debug(
+                    f"File already tracked, skipping duplicate count: {file_path}"
+                )
+            else:
+                # No valid file path found
+                self.logger.warning("No valid file path found for tracking")
+
+        except Exception as e:
+            self.logger.warning(f"Failed to track file implementation: {e}")
+            # Even if tracking fails, try to count based on tool input (but check for duplicates)
+
+            file_path = tool_call["input"].get("file_path")
+            if file_path and file_path not in self.implemented_files_set:
+                self.implemented_files_set.add(file_path)
+                self.files_implemented_count += 1
+                self.logger.info(
+                    f"File implementation counted (emergency fallback): count={self.files_implemented_count}, file={file_path}"
+                )
+
+    def _track_dependency_analysis(self, tool_call: Dict, result: Any):
+        """
+        Track dependency analysis through read_file calls
+        """
+        try:
+            file_path = tool_call["input"].get("file_path")
+            if file_path:
+                # Track unique files read for dependency analysis
+                if file_path not in self.files_read_for_dependencies:
+                    self.files_read_for_dependencies.add(file_path)
+
+                    # Add to dependency analysis summary
+                    self.implementation_summary["dependency_analysis"].append(
+                        {
+                            "file_read": file_path,
+                            "timestamp": time.time(),
+                            "purpose": "dependency_analysis",
+                        }
+                    )
+
+                    self.logger.info(
+                        f"Dependency analysis tracked: file_read={file_path}"
+                    )
+
+        except Exception as e:
+            self.logger.warning(f"Failed to track dependency analysis: {e}")
+
+    def calculate_messages_token_count(self, messages: List[Dict]) -> int:
+        """
+        Calculate total token count for a list of messages
+
+        Args:
+            messages: List of chat messages with 'role' and 'content' keys
+
+        Returns:
+            Total token count
+        """
+        if not self.tokenizer:
+            # Fallback: rough estimation based on character count
+            total_chars = sum(len(str(msg.get("content", ""))) for msg in messages)
+            # Rough approximation: 1 token ≈ 4 characters
+            return total_chars // 4
+
+        try:
+            total_tokens = 0
+            for message in messages:
+                content = str(message.get("content", ""))
+                role = message.get("role", "")
+
+                # Count tokens for content
+                if content:
+                    content_tokens = len(
+                        self.tokenizer.encode(content, disallowed_special=())
+                    )
+                    total_tokens += content_tokens
+
+                # Add tokens for role and message structure
+                role_tokens = len(self.tokenizer.encode(role, disallowed_special=()))
+                total_tokens += role_tokens + 4  # Extra tokens for message formatting
+
+            return total_tokens
+
+        except Exception as e:
+            self.logger.warning(f"Token calculation failed: {e}")
+            # Fallback estimation
+            total_chars = sum(len(str(msg.get("content", ""))) for msg in messages)
+            return total_chars // 4
+
+    def should_trigger_summary_by_tokens(self, messages: List[Dict]) -> bool:
+        """
+        Check if summary should be triggered based on token count
+
+        Args:
+            messages: Current conversation messages
+
+        Returns:
+            True if summary should be triggered based on token count
+        """
+        if not messages:
+            return False
+
+        # Calculate current token count / 计算当前token数
+        current_token_count = self.calculate_messages_token_count(messages)
+
+        # Check if we should trigger summary / 检查是否应触发总结
+        should_trigger = (
+            current_token_count > self.summary_trigger_tokens
+            and current_token_count
+            > self.last_summary_token_count
+            + 10000  # Minimum 10k tokens between summaries / 总结间最少10k tokens
+        )
+
+        if should_trigger:
+            self.logger.info(
+                f"Token-based summary trigger: current={current_token_count:,}, "
+                f"threshold={self.summary_trigger_tokens:,}, "
+                f"last_summary={self.last_summary_token_count:,}"
+            )
+
+        return should_trigger
+
+    def should_trigger_summary(
+        self, summary_trigger: int = 5, messages: List[Dict] = None
+    ) -> bool:
+        """
+        Check if summary should be triggered based on token count (preferred) or file count (fallback)
+        根据token数（首选）或文件数（回退）检查是否应触发总结
+
+        Args:
+            summary_trigger: Number of files after which to trigger summary (fallback)
+            messages: Current conversation messages for token calculation
+
+        Returns:
+            True if summary should be triggered
+        """
+        # Primary: Token-based triggering / 主要：基于token的触发
+        if messages and self.tokenizer:
+            return self.should_trigger_summary_by_tokens(messages)
+
+        # Fallback: File-based triggering (original logic) / 回退：基于文件的触发（原始逻辑）
+        self.logger.info("Using fallback file-based summary triggering")
+        should_trigger = (
+            self.files_implemented_count > 0
+            and self.files_implemented_count % summary_trigger == 0
+            and self.files_implemented_count > self.last_summary_file_count
+        )
+
+        return should_trigger
+
+    def mark_summary_triggered(self, messages: List[Dict] = None):
+        """
+        Mark that summary has been triggered for current state
+        标记当前状态的总结已被触发
+
+        Args:
+            messages: Current conversation messages for token tracking
+        """
+        # Update file-based tracking / 更新基于文件的跟踪
+        self.last_summary_file_count = self.files_implemented_count
+
+        # Update token-based tracking / 更新基于token的跟踪
+        if messages and self.tokenizer:
+            self.last_summary_token_count = self.calculate_messages_token_count(
+                messages
+            )
+            self.logger.info(
+                f"Summary marked as triggered - file_count: {self.files_implemented_count}, "
+                f"token_count: {self.last_summary_token_count:,}"
+            )
+        else:
+            self.logger.info(
+                f"Summary marked as triggered for file count: {self.files_implemented_count}"
+            )
+
+    def get_implementation_summary(self) -> Dict[str, Any]:
+        """
+        Get current implementation summary
+        获取当前实现总结
+        """
+        return self.implementation_summary.copy()
+
+    def get_files_implemented_count(self) -> int:
+        """
+        Get the number of files implemented so far
+        获取到目前为止实现的文件数量
+        """
+        return self.files_implemented_count
+
+    def get_read_tools_status(self) -> Dict[str, Any]:
+        """
+        Get read tools configuration status
+        获取读取工具配置状态
+
+        Returns:
+            Dictionary with read tools status information
+        """
+        return {
+            "read_tools_enabled": self.enable_read_tools,
+            "status": "ENABLED" if self.enable_read_tools else "DISABLED",
+            "tools_affected": ["read_file", "read_code_mem"],
+            "description": "Read tools configuration for testing purposes",
+        }
+
+    def add_technical_decision(self, decision: str, context: str = ""):
+        """
+        Add a technical decision to the implementation summary
+        向实现总结添加技术决策
+
+        Args:
+            decision: Description of the technical decision
+            context: Additional context for the decision
+        """
+        self.implementation_summary["technical_decisions"].append(
+            {"decision": decision, "context": context, "timestamp": time.time()}
+        )
+        self.logger.info(f"Technical decision recorded: {decision}")
+
+    def add_constraint(self, constraint: str, impact: str = ""):
+        """
+        Add an important constraint to the implementation summary
+        向实现总结添加重要约束
+
+        Args:
+            constraint: Description of the constraint
+            impact: Impact of the constraint on implementation
+        """
+        self.implementation_summary["important_constraints"].append(
+            {"constraint": constraint, "impact": impact, "timestamp": time.time()}
+        )
+        self.logger.info(f"Constraint recorded: {constraint}")
+
+    def add_architecture_note(self, note: str, component: str = ""):
+        """
+        Add an architecture note to the implementation summary
+        向实现总结添加架构注释
+
+        Args:
+            note: Architecture note description
+            component: Related component or module
+        """
+        self.implementation_summary["architecture_notes"].append(
+            {"note": note, "component": component, "timestamp": time.time()}
+        )
+        self.logger.info(f"Architecture note recorded: {note}")
+
+    def get_implementation_statistics(self) -> Dict[str, Any]:
+        """
+        Get comprehensive implementation statistics
+        获取全面的实现统计信息
+        """
+        return {
+            "total_files_implemented": self.files_implemented_count,
+            "files_implemented_count": self.files_implemented_count,
+            "technical_decisions_count": len(
+                self.implementation_summary["technical_decisions"]
+            ),
+            "constraints_count": len(
+                self.implementation_summary["important_constraints"]
+            ),
+            "architecture_notes_count": len(
+                self.implementation_summary["architecture_notes"]
+            ),
+            "dependency_analysis_count": len(
+                self.implementation_summary["dependency_analysis"]
+            ),
+            "files_read_for_dependencies": len(self.files_read_for_dependencies),
+            "unique_files_implemented": len(self.implemented_files_set),
+            "completed_files_list": [
+                f["file"] for f in self.implementation_summary["completed_files"]
+            ],
+            "dependency_files_read": list(self.files_read_for_dependencies),
+            "last_summary_file_count": self.last_summary_file_count,
+            "read_tools_status": self.get_read_tools_status(),  # Include read tools configuration
+        }
+
+    def force_enable_optimization(self):
+        """
+        Force enable optimization for testing purposes
+        强制启用优化用于测试目的
+        """
+        self.files_implemented_count = 1
+        self.logger.info(
+            f"🔧 OPTIMIZATION FORCE ENABLED: files_implemented_count set to {self.files_implemented_count}"
+        )
+        print(
+            f"🔧 OPTIMIZATION FORCE ENABLED: files_implemented_count set to {self.files_implemented_count}"
+        )
+
+    def reset_implementation_tracking(self):
+        """
+        Reset implementation tracking (useful for new sessions)
+        重置实现跟踪（对新会话有用）
+        """
+        self.implementation_summary = {
+            "completed_files": [],
+            "technical_decisions": [],
+            "important_constraints": [],
+            "architecture_notes": [],
+            "dependency_analysis": [],  # Reset dependency analysis and file reads
+        }
+        self.files_implemented_count = 0
+        self.implemented_files_set = (
+            set()
+        )  # Reset the unique files set / 重置唯一文件集合
+        self.files_read_for_dependencies = (
+            set()
+        )  # Reset files read for dependency analysis / 重置为依赖分析而读取的文件
+        self.last_summary_file_count = 0  # Reset the file count when last summary was triggered / 重置上次触发总结时的文件数
+        self.last_summary_token_count = 0  # Reset token count when last summary was triggered / 重置上次触发总结时的token数
+        self.logger.info("Implementation tracking reset")
+
+        # Reset analysis loop detection / 重置分析循环检测
+        self.recent_tool_calls = []
+        self.logger.info("Analysis loop detection reset")
+
+    def _track_tool_call_for_loop_detection(self, tool_name: str):
+        """
+        Track tool calls for analysis loop detection
+        跟踪工具调用以检测分析循环
+
+        Args:
+            tool_name: Name of the tool called
+        """
+        self.recent_tool_calls.append(tool_name)
+        if len(self.recent_tool_calls) > self.max_read_without_write:
+            self.recent_tool_calls.pop(0)
+
+        if len(set(self.recent_tool_calls)) == 1:
+            self.logger.warning("Analysis loop detected")
+
+    def is_in_analysis_loop(self) -> bool:
+        """
+        Check if the agent is in an analysis loop (only reading files, not writing)
+        检查代理是否在分析循环中（只读文件，不写文件）
+
+        Returns:
+            True if in analysis loop
+        """
+        if len(self.recent_tool_calls) < self.max_read_without_write:
+            return False
+
+        # Check if recent calls are all read_file or search_reference_code / 检查最近的调用是否都是read_file或search_reference_code
+        analysis_tools = {
+            "read_file",
+            "search_reference_code",
+            "get_all_available_references",
+        }
+        recent_calls_set = set(self.recent_tool_calls)
+
+        # If all recent calls are analysis tools, we're in an analysis loop / 如果最近的调用都是分析工具，我们在分析循环中
+        in_loop = (
+            recent_calls_set.issubset(analysis_tools) and len(recent_calls_set) >= 1
+        )
+
+        if in_loop:
+            self.logger.warning(
+                f"Analysis loop detected! Recent calls: {self.recent_tool_calls}"
+            )
+
+        return in_loop
+
+    def get_analysis_loop_guidance(self) -> str:
+        """
+        Get guidance to break out of analysis loop
+        获取跳出分析循环的指导
+
+        Returns:
+            Guidance message to encourage implementation
+        """
+        return f"""🚨 **ANALYSIS LOOP DETECTED - IMMEDIATE ACTION REQUIRED**
+
+**Problem**: You've been reading/analyzing files for {len(self.recent_tool_calls)} consecutive calls without writing code.
+**Recent tool calls**: {' → '.join(self.recent_tool_calls)}
+
+**SOLUTION - IMPLEMENT CODE NOW**:
+1. **STOP ANALYZING** - You have enough information
+2. **Use write_file** to create the next code file according to the implementation plan
+3. **Choose ANY file** from the plan that hasn't been implemented yet
+4. **Write complete, working code** - don't ask for permission or clarification
+
+**Files implemented so far**: {self.files_implemented_count}
+**Your goal**: Implement MORE files, not analyze existing ones!
+
+**CRITICAL**: Your next response MUST use write_file to create a new code file!"""
+
+    async def test_summary_functionality(self, test_file_path: str = None):
+        """
+        Test if the code summary functionality is working correctly
+        测试代码总结功能是否正常工作
+
+        Args:
+            test_file_path: Specific file to test, if None will test all implemented files
+        """
+        if not self.memory_agent:
+            self.logger.warning("No memory agent available for testing")
+            return
+
+        if test_file_path:
+            files_to_test = [test_file_path]
+        else:
+            # Use implemented files from tracking
+            files_to_test = list(self.implemented_files_set)[
+                :3
+            ]  # Limit to first 3 files
+
+        if not files_to_test:
+            self.logger.warning("No implemented files to test")
+            return
+
+        # Test each file silently
+        summary_files_found = 0
+
+        for file_path in files_to_test:
+            if self.mcp_agent:
+                try:
+                    result = await self.mcp_agent.call_tool(
+                        "read_code_mem", {"file_paths": [file_path]}
+                    )
+
+                    # Parse the result to check if summary was found
+                    import json
+
+                    result_data = (
+                        json.loads(result) if isinstance(result, str) else result
+                    )
+
+                    if (
+                        result_data.get("status")
+                        in ["all_summaries_found", "partial_summaries_found"]
+                        and result_data.get("summaries_found", 0) > 0
+                    ):
+                        summary_files_found += 1
+                except Exception as e:
+                    self.logger.warning(
+                        f"Failed to test read_code_mem for {file_path}: {e}"
+                    )
+            else:
+                self.logger.warning("MCP agent not available for testing")
+
+        self.logger.info(
+            f"📋 Summary testing: {summary_files_found}/{len(files_to_test)} files have summaries"
+        )
+
+    async def test_automatic_read_file_optimization(self):
+        """
+        Test the automatic read_file optimization that redirects to read_code_mem
+        测试自动read_file优化，重定向到read_code_mem
+        """
+        print("=" * 80)
+        print("🔄 TESTING AUTOMATIC READ_FILE OPTIMIZATION")
+        print("=" * 80)
+
+        # Simulate that at least one file has been implemented (to trigger optimization)
+        self.files_implemented_count = 1
+
+        # Test with a generic config file that should have a summary
+        test_file = "config.py"
+
+        print(f"📁 Testing automatic optimization for: {test_file}")
+        print(f"📊 Files implemented count: {self.files_implemented_count}")
+        print(
+            f"🔧 Optimization should be: {'ENABLED' if self.files_implemented_count > 0 else 'DISABLED'}"
+        )
+
+        # Create a simulated read_file tool call
+        simulated_read_file_call = {
+            "id": "test_read_file_optimization",
+            "name": "read_file",
+            "input": {"file_path": test_file},
+        }
+
+        print("\n🔄 Simulating read_file call:")
+        print(f"   Tool: {simulated_read_file_call['name']}")
+        print(f"   File: {simulated_read_file_call['input']['file_path']}")
+
+        # Execute the tool call (this should trigger automatic optimization)
+        results = await self.execute_tool_calls([simulated_read_file_call])
+
+        if results:
+            result = results[0]
+            print("\n✅ Tool execution completed:")
+            print(f"   Tool name: {result.get('tool_name', 'N/A')}")
+            print(f"   Tool ID: {result.get('tool_id', 'N/A')}")
+
+            # Parse the result to check if optimization occurred
+            import json
+
+            try:
+                result_data = json.loads(result.get("result", "{}"))
+                if result_data.get("optimization") == "redirected_to_read_code_mem":
+                    print("🎉 SUCCESS: read_file was automatically optimized!")
+                    print(
+                        f"   Original tool: {result_data.get('original_tool', 'N/A')}"
+                    )
+                    print(f"   Status: {result_data.get('status', 'N/A')}")
+                elif result_data.get("status") == "summary_found":
+                    print("🎉 SUCCESS: Summary was found and returned!")
+                else:
+                    print("ℹ️  INFO: No optimization occurred (no summary available)")
+            except json.JSONDecodeError:
+                print("⚠️  WARNING: Could not parse result as JSON")
+        else:
+            print("❌ ERROR: No results returned from tool execution")
+
+        print("\n" + "=" * 80)
+        print("🔄 AUTOMATIC READ_FILE OPTIMIZATION TEST COMPLETE")
+        print("=" * 80)
+
+    async def test_summary_optimization(self, test_file_path: str = "config.py"):
+        """
+        Test the summary optimization functionality with a specific file
+        测试特定文件的总结优化功能
+
+        Args:
+            test_file_path: File path to test (default: config.py which should be in summary)
+        """
+        if not self.mcp_agent:
+            return False
+
+        try:
+            # Use MCP agent to call read_code_mem tool
+            result = await self.mcp_agent.call_tool(
+                "read_code_mem", {"file_paths": [test_file_path]}
+            )
+
+            # Parse the result to check if summary was found
+            import json
+
+            result_data = json.loads(result) if isinstance(result, str) else result
+
+            return (
+                result_data.get("status")
+                in ["all_summaries_found", "partial_summaries_found"]
+                and result_data.get("summaries_found", 0) > 0
+            )
+        except Exception as e:
+            self.logger.warning(f"Failed to test read_code_mem optimization: {e}")
+            return False
+
+    async def test_read_tools_configuration(self):
+        """
+        Test the read tools configuration to verify enabling/disabling works correctly
+        测试读取工具配置以验证启用/禁用是否正常工作
+        """
+        print("=" * 60)
+        print("🧪 TESTING READ TOOLS CONFIGURATION")
+        print("=" * 60)
+
+        status = self.get_read_tools_status()
+        print(f"Read tools enabled: {status['read_tools_enabled']}")
+        print(f"Status: {status['status']}")
+        print(f"Tools affected: {status['tools_affected']}")
+
+        # Test with mock tool calls
+        test_tools = [
+            {
+                "id": "test_read_file",
+                "name": "read_file",
+                "input": {"file_path": "test.py"},
+            },
+            {
+                "id": "test_read_code_mem",
+                "name": "read_code_mem",
+                "input": {"file_path": "test.py"},
+            },
+            {
+                "id": "test_write_file",
+                "name": "write_file",
+                "input": {"file_path": "test.py", "content": "# test"},
+            },
+        ]
+
+        print(
+            f"\n🔄 Testing tool execution with read_tools_enabled={self.enable_read_tools}"
+        )
+
+        for tool_call in test_tools:
+            tool_name = tool_call["name"]
+            if not self.enable_read_tools and tool_name in [
+                "read_file",
+                "read_code_mem",
+            ]:
+                print(f"🚫 {tool_name}: Would be SKIPPED (disabled)")
+            else:
+                print(f"✅ {tool_name}: Would be EXECUTED")
+
+        print("=" * 60)
+        print("🧪 READ TOOLS CONFIGURATION TEST COMPLETE")
+        print("=" * 60)
+
+        return status

projects/ui/DeepCode/workflows/agents/document_segmentation_agent.py

@@ -0,0 +1,353 @@
+"""
+Document Segmentation Agent
+
+A lightweight agent that coordinates with the document segmentation MCP server
+to analyze document structure and prepare segments for other agents.
+"""
+
+import os
+import logging
+from typing import Dict, Any, Optional
+
+from mcp_agent.agents.agent import Agent
+from utils.llm_utils import get_preferred_llm_class
+
+
+class DocumentSegmentationAgent:
+    """
+    Intelligent document segmentation agent with semantic analysis capabilities.
+
+    This enhanced agent provides:
+    1. **Semantic Document Classification**: Content-based document type identification
+    2. **Adaptive Segmentation Strategy**: Algorithm integrity and semantic coherence preservation
+    3. **Planning Agent Optimization**: Segment preparation specifically optimized for downstream agents
+    4. **Quality Intelligence Validation**: Advanced metrics for completeness and technical accuracy
+    5. **Algorithm Completeness Protection**: Ensures critical algorithms and formulas remain intact
+
+    Key improvements over traditional segmentation:
+    - Semantic content analysis vs mechanical structure splitting
+    - Dynamic character limits based on content complexity
+    - Enhanced relevance scoring for planning agents
+    - Algorithm and formula integrity preservation
+    - Content type-aware segmentation strategies
+    """
+
+    def __init__(self, logger: Optional[logging.Logger] = None):
+        self.logger = logger or self._create_default_logger()
+        self.mcp_agent = None
+
+    def _create_default_logger(self) -> logging.Logger:
+        """Create default logger if none provided"""
+        logger = logging.getLogger(f"{__name__}.DocumentSegmentationAgent")
+        logger.setLevel(logging.INFO)
+        return logger
+
+    async def __aenter__(self):
+        """Async context manager entry"""
+        await self.initialize()
+        return self
+
+    async def __aexit__(self, exc_type, exc_val, exc_tb):
+        """Async context manager exit"""
+        await self.cleanup()
+
+    async def initialize(self):
+        """Initialize the MCP agent connection"""
+        try:
+            self.mcp_agent = Agent(
+                name="DocumentSegmentationCoordinator",
+                instruction="""You are an intelligent document segmentation coordinator that leverages advanced semantic analysis for optimal document processing.
+
+Your enhanced capabilities include:
+1. **Semantic Content Analysis**: Coordinate intelligent document type classification based on content semantics rather than structural patterns
+2. **Algorithm Integrity Protection**: Ensure algorithm blocks, formulas, and related content maintain logical coherence
+3. **Adaptive Segmentation Strategy**: Select optimal segmentation approaches (semantic_research_focused, algorithm_preserve_integrity, concept_implementation_hybrid, etc.)
+4. **Quality Intelligence Validation**: Assess segmentation quality using enhanced metrics for completeness, relevance, and technical accuracy
+5. **Planning Agent Optimization**: Ensure segments are specifically optimized for ConceptAnalysisAgent, AlgorithmAnalysisAgent, and CodePlannerAgent needs
+
+**Key Principles**:
+- Prioritize content semantics over mechanical structure
+- Preserve algorithm and formula completeness
+- Optimize for downstream agent token efficiency
+- Ensure technical content integrity
+- Provide actionable quality assessments
+
+Use the enhanced document-segmentation tools to deliver superior segmentation results that significantly improve planning agent performance.""",
+                server_names=["document-segmentation", "filesystem"],
+            )
+
+            # Initialize the agent context
+            await self.mcp_agent.__aenter__()
+
+            # Attach LLM
+            self.llm = await self.mcp_agent.attach_llm(get_preferred_llm_class())
+
+            self.logger.info("DocumentSegmentationAgent initialized successfully")
+
+        except Exception as e:
+            self.logger.error(f"Failed to initialize DocumentSegmentationAgent: {e}")
+            raise
+
+    async def cleanup(self):
+        """Cleanup resources"""
+        if self.mcp_agent:
+            try:
+                await self.mcp_agent.__aexit__(None, None, None)
+            except Exception as e:
+                self.logger.warning(f"Error during cleanup: {e}")
+
+    async def analyze_and_prepare_document(
+        self, paper_dir: str, force_refresh: bool = False
+    ) -> Dict[str, Any]:
+        """
+        Perform intelligent semantic analysis and create optimized document segments.
+
+        This method coordinates with the enhanced document segmentation server to:
+        - Classify document type using semantic content analysis
+        - Select optimal segmentation strategy (semantic_research_focused, algorithm_preserve_integrity, etc.)
+        - Preserve algorithm and formula integrity
+        - Optimize segments for downstream planning agents
+
+        Args:
+            paper_dir: Path to the paper directory
+            force_refresh: Whether to force re-analysis with latest algorithms
+
+        Returns:
+            Dict containing enhanced analysis results and intelligent segment information
+        """
+        try:
+            self.logger.info(f"Starting document analysis for: {paper_dir}")
+
+            # Check if markdown file exists
+            md_files = [f for f in os.listdir(paper_dir) if f.endswith(".md")]
+            if not md_files:
+                raise ValueError(f"No markdown file found in {paper_dir}")
+
+            # Use the enhanced document segmentation tool
+            message = f"""Please perform intelligent semantic analysis and segmentation for the document in directory: {paper_dir}
+
+Use the analyze_and_segment_document tool with these parameters:
+- paper_dir: {paper_dir}
+- force_refresh: {force_refresh}
+
+**Focus on these enhanced objectives**:
+1. **Semantic Document Classification**: Identify document type using content semantics (research_paper, algorithm_focused, technical_doc, etc.)
+2. **Intelligent Segmentation Strategy**: Select the optimal strategy based on content analysis:
+   - `semantic_research_focused` for research papers with high algorithm density
+   - `algorithm_preserve_integrity` for algorithm-heavy documents
+   - `concept_implementation_hybrid` for mixed concept/implementation content
+3. **Algorithm Completeness**: Ensure algorithm blocks, formulas, and related descriptions remain logically connected
+4. **Planning Agent Optimization**: Create segments that maximize effectiveness for ConceptAnalysisAgent, AlgorithmAnalysisAgent, and CodePlannerAgent
+
+After segmentation, get a document overview and provide:
+- Quality assessment of semantic segmentation approach
+- Algorithm/formula integrity verification
+- Recommendations for planning agent optimization
+- Technical content completeness evaluation"""
+
+            result = await self.llm.generate_str(message=message)
+
+            self.logger.info("Document analysis completed successfully")
+
+            # Parse the result and return structured information
+            return {
+                "status": "success",
+                "paper_dir": paper_dir,
+                "analysis_result": result,
+                "segments_available": True,
+            }
+
+        except Exception as e:
+            self.logger.error(f"Error in document analysis: {e}")
+            return {
+                "status": "error",
+                "paper_dir": paper_dir,
+                "error_message": str(e),
+                "segments_available": False,
+            }
+
+    async def get_document_overview(self, paper_dir: str) -> Dict[str, Any]:
+        """
+        Get overview of document structure and segments.
+
+        Args:
+            paper_dir: Path to the paper directory
+
+        Returns:
+            Dict containing document overview information
+        """
+        try:
+            message = f"""Please provide an intelligent overview of the enhanced document segmentation for: {paper_dir}
+
+Use the get_document_overview tool to retrieve:
+- **Semantic Document Classification**: Document type and confidence score
+- **Adaptive Segmentation Strategy**: Strategy used and reasoning
+- **Segment Intelligence**: Total segments with enhanced metadata
+- **Content Type Distribution**: Breakdown by algorithm, concept, formula, implementation content
+- **Quality Intelligence Assessment**: Completeness, coherence, and planning agent optimization
+
+Provide a comprehensive analysis focusing on:
+1. Semantic vs structural segmentation quality
+2. Algorithm and formula integrity preservation
+3. Segment relevance for downstream planning agents
+4. Technical content distribution and completeness"""
+
+            result = await self.llm.generate_str(message=message)
+
+            return {
+                "status": "success",
+                "paper_dir": paper_dir,
+                "overview_result": result,
+            }
+
+        except Exception as e:
+            self.logger.error(f"Error getting document overview: {e}")
+            return {"status": "error", "paper_dir": paper_dir, "error_message": str(e)}
+
+    async def validate_segmentation_quality(self, paper_dir: str) -> Dict[str, Any]:
+        """
+        Validate the quality of document segmentation.
+
+        Args:
+            paper_dir: Path to the paper directory
+
+        Returns:
+            Dict containing validation results
+        """
+        try:
+            # Get overview first
+            overview_result = await self.get_document_overview(paper_dir)
+
+            if overview_result["status"] != "success":
+                return overview_result
+
+            # Analyze enhanced segmentation quality
+            message = f"""Based on the intelligent document overview for {paper_dir}, please evaluate the enhanced segmentation quality using advanced criteria.
+
+**Enhanced Quality Assessment Factors**:
+1. **Semantic Coherence**: Do segments maintain logical content boundaries vs mechanical structural splits?
+2. **Algorithm Integrity**: Are algorithm blocks, formulas, and related explanations kept together?
+3. **Content Type Optimization**: Are different content types (algorithm, concept, formula, implementation) properly identified and scored?
+4. **Planning Agent Effectiveness**: Will ConceptAnalysisAgent, AlgorithmAnalysisAgent, and CodePlannerAgent receive optimal information?
+5. **Dynamic Sizing**: Are segments adaptively sized based on content complexity rather than fixed limits?
+6. **Technical Completeness**: Are critical technical details preserved without fragmentation?
+
+**Provide specific recommendations for**:
+- Semantic segmentation improvements
+- Algorithm/formula integrity enhancements
+- Planning agent optimization opportunities
+- Content distribution balance adjustments"""
+
+            validation_result = await self.llm.generate_str(message=message)
+
+            return {
+                "status": "success",
+                "paper_dir": paper_dir,
+                "validation_result": validation_result,
+                "overview_data": overview_result,
+            }
+
+        except Exception as e:
+            self.logger.error(f"Error validating segmentation quality: {e}")
+            return {"status": "error", "paper_dir": paper_dir, "error_message": str(e)}
+
+
+async def run_document_segmentation_analysis(
+    paper_dir: str, logger: Optional[logging.Logger] = None, force_refresh: bool = False
+) -> Dict[str, Any]:
+    """
+    Convenience function to run document segmentation analysis.
+
+    Args:
+        paper_dir: Path to the paper directory
+        logger: Optional logger instance
+        force_refresh: Whether to force re-analysis
+
+    Returns:
+        Dict containing analysis results
+    """
+    async with DocumentSegmentationAgent(logger=logger) as agent:
+        # Analyze and prepare document
+        analysis_result = await agent.analyze_and_prepare_document(
+            paper_dir, force_refresh=force_refresh
+        )
+
+        if analysis_result["status"] == "success":
+            # Validate segmentation quality
+            validation_result = await agent.validate_segmentation_quality(paper_dir)
+            analysis_result["validation"] = validation_result
+
+        return analysis_result
+
+
+# Utility function for integration with existing workflow
+async def prepare_document_segments(
+    paper_dir: str, logger: Optional[logging.Logger] = None
+) -> Dict[str, Any]:
+    """
+    Prepare intelligent document segments optimized for planning agents.
+
+    This enhanced function leverages semantic analysis to create segments that:
+    - Preserve algorithm and formula integrity
+    - Optimize for ConceptAnalysisAgent, AlgorithmAnalysisAgent, and CodePlannerAgent
+    - Use adaptive character limits based on content complexity
+    - Maintain technical content completeness
+
+    Called from the orchestration engine (Phase 3.5) to prepare documents
+    before the planning phase with superior segmentation quality.
+
+    Args:
+        paper_dir: Path to the paper directory containing markdown file
+        logger: Optional logger instance for tracking
+
+    Returns:
+        Dict containing enhanced preparation results and intelligent metadata
+    """
+    try:
+        logger = logger or logging.getLogger(__name__)
+        logger.info(f"Preparing document segments for: {paper_dir}")
+
+        # Run analysis
+        result = await run_document_segmentation_analysis(
+            paper_dir=paper_dir,
+            logger=logger,
+            force_refresh=False,  # Use cached analysis if available
+        )
+
+        if result["status"] == "success":
+            logger.info("Document segments prepared successfully")
+
+            # Create metadata for downstream agents
+            segments_dir = os.path.join(paper_dir, "document_segments")
+
+            return {
+                "status": "success",
+                "paper_dir": paper_dir,
+                "segments_dir": segments_dir,
+                "segments_ready": True,
+                "analysis_summary": result.get("analysis_result", ""),
+                "validation_summary": result.get("validation", {}).get(
+                    "validation_result", ""
+                ),
+            }
+        else:
+            logger.error(
+                f"Document segmentation failed: {result.get('error_message', 'Unknown error')}"
+            )
+            return {
+                "status": "error",
+                "paper_dir": paper_dir,
+                "segments_ready": False,
+                "error_message": result.get(
+                    "error_message", "Document segmentation failed"
+                ),
+            }
+
+    except Exception as e:
+        logger.error(f"Error preparing document segments: {e}")
+        return {
+            "status": "error",
+            "paper_dir": paper_dir,
+            "segments_ready": False,
+            "error_message": str(e),
+        }

projects/ui/DeepCode/workflows/agents/memory_agent_concise.py

@@ -0,0 +1,1489 @@
+"""
+Concise Memory Agent for Code Implementation Workflow
+
+This memory agent implements a focused approach:
+1. Before first file: Normal conversation flow
+2. After first file: Keep only system_prompt + initial_plan + current round tool results
+3. Clean slate for each new code file generation
+
+Key Features:
+- Preserves system prompt and initial plan always
+- After first file generation, discards previous conversation history
+- Keeps only current round tool results from essential tools:
+  * read_code_mem, read_file, write_file
+  * execute_python, execute_bash
+  * search_code, search_reference_code, get_file_structure
+- Provides clean, focused input for next write_file operation
+"""
+
+import json
+import logging
+import os
+import time
+from datetime import datetime
+from typing import Dict, Any, List, Optional
+
+
+class ConciseMemoryAgent:
+    """
+    Concise Memory Agent - Focused Information Retention
+
+    Core Philosophy:
+    - Preserve essential context (system prompt + initial plan)
+    - After first file generation, use clean slate approach
+    - Keep only current round tool results from all essential MCP tools
+    - Remove conversational clutter and previous tool calls
+
+    Essential Tools Tracked:
+    - File Operations: read_code_mem, read_file, write_file
+    - Code Analysis: search_code, search_reference_code, get_file_structure
+    - Execution: execute_python, execute_bash
+    """
+
+    def __init__(
+        self,
+        initial_plan_content: str,
+        logger: Optional[logging.Logger] = None,
+        target_directory: Optional[str] = None,
+        default_models: Optional[Dict[str, str]] = None,
+    ):
+        """
+        Initialize Concise Memory Agent
+
+        Args:
+            initial_plan_content: Content of initial_plan.txt
+            logger: Logger instance
+            target_directory: Target directory for saving summaries
+            default_models: Default models configuration from workflow
+        """
+        self.logger = logger or self._create_default_logger()
+        self.initial_plan = initial_plan_content
+
+        # Store default models configuration
+        self.default_models = default_models or {
+            "anthropic": "claude-sonnet-4-20250514",
+            "openai": "gpt-4o",
+        }
+
+        # Memory state tracking - new logic: trigger after each write_file
+        self.last_write_file_detected = (
+            False  # Track if write_file was called in current iteration
+        )
+        self.should_clear_memory_next = False  # Flag to clear memory in next round
+        self.current_round = 0
+
+        # Parse phase structure from initial plan
+        self.phase_structure = self._parse_phase_structure()
+
+        # Extract all files from file structure in initial plan
+        self.all_files_list = self._extract_all_files_from_plan()
+
+        # Memory configuration
+        if target_directory:
+            self.save_path = target_directory
+        else:
+            self.save_path = "./deepcode_lab/papers/1/"
+
+        # Code summary file path
+        self.code_summary_path = os.path.join(
+            self.save_path, "implement_code_summary.md"
+        )
+
+        # Current round tool results storage
+        self.current_round_tool_results = []
+
+        # Track all implemented files
+        self.implemented_files = []
+
+        # Store Next Steps information temporarily (not saved to file)
+        self.current_next_steps = ""
+
+        self.logger.info(
+            f"Concise Memory Agent initialized with target directory: {self.save_path}"
+        )
+        self.logger.info(f"Code summary will be saved to: {self.code_summary_path}")
+        # self.logger.info(f"🤖 Using models - Anthropic: {self.default_models['anthropic']}, OpenAI: {self.default_models['openai']}")
+        self.logger.info(
+            "📝 NEW LOGIC: Memory clearing triggered after each write_file call"
+        )
+
+    def _create_default_logger(self) -> logging.Logger:
+        """Create default logger"""
+        logger = logging.getLogger(f"{__name__}.ConciseMemoryAgent")
+        logger.setLevel(logging.INFO)
+        return logger
+
+    def _parse_phase_structure(self) -> Dict[str, List[str]]:
+        """Parse implementation phases from initial plan"""
+        try:
+            phases = {}
+            lines = self.initial_plan.split("\n")
+            current_phase = None
+
+            for line in lines:
+                if "Phase" in line and ":" in line:
+                    # Extract phase name
+                    phase_parts = line.split(":")
+                    if len(phase_parts) >= 2:
+                        current_phase = phase_parts[0].strip()
+                        phases[current_phase] = []
+                elif current_phase and line.strip().startswith("-"):
+                    # This is a file in the current phase
+                    file_line = line.strip()[1:].strip()
+                    if file_line.startswith("`") and file_line.endswith("`"):
+                        file_name = file_line[1:-1]
+                        phases[current_phase].append(file_name)
+                elif current_phase and not line.strip():
+                    # Empty line might indicate end of phase
+                    continue
+                elif current_phase and line.strip().startswith("###"):
+                    # New section, end current phase
+                    current_phase = None
+
+            return phases
+
+        except Exception as e:
+            self.logger.warning(f"Failed to parse phase structure: {e}")
+            return {}
+
+    def _extract_all_files_from_plan(self) -> List[str]:
+        """
+        Extract all file paths from the file_structure section in initial plan
+        Handles multiple formats: tree structure, YAML, and simple lists
+
+        Returns:
+            List of all file paths that should be implemented
+        """
+        try:
+            lines = self.initial_plan.split("\n")
+            files = []
+
+            # Method 1: Try to extract from tree structure in file_structure section
+            files.extend(self._extract_from_tree_structure(lines))
+
+            # Method 2: If no files found, try to extract from simple list format
+            if not files:
+                files.extend(self._extract_from_simple_list(lines))
+
+            # Method 3: If still no files, try to extract from anywhere in the plan
+            if not files:
+                files.extend(self._extract_from_plan_content(lines))
+
+            # Clean and validate file paths
+            cleaned_files = self._clean_and_validate_files(files)
+
+            # Log the extracted files
+            self.logger.info(
+                f"📁 Extracted {len(cleaned_files)} files from initial plan"
+            )
+            if cleaned_files:
+                self.logger.info(f"📁 Sample files: {cleaned_files[:3]}...")
+
+            return cleaned_files
+
+        except Exception as e:
+            self.logger.error(f"Failed to extract files from initial plan: {e}")
+            return []
+
+    def _extract_from_tree_structure(self, lines: List[str]) -> List[str]:
+        """Extract files from tree structure format - only from file_structure section"""
+        files = []
+        in_file_structure = False
+        path_stack = []
+
+        for line in lines:
+            # Check if we're in the file_structure section
+            if "file_structure:" in line or "file_structure |" in line:
+                in_file_structure = True
+                continue
+            # Check for end of file_structure section (next YAML key)
+            elif (
+                in_file_structure
+                and line.strip()
+                and not line.startswith(" ")
+                and ":" in line
+            ):
+                # This looks like a new YAML section, stop parsing
+                break
+            elif not in_file_structure:
+                continue
+
+            if not line.strip():
+                continue
+
+            # Skip lines that look like YAML keys (contain ":" but not file paths)
+            if ":" in line and not ("." in line and "/" in line):
+                continue
+
+            stripped_line = line.strip()
+
+            # Detect root directory (directory name ending with / at minimal indentation)
+            if (
+                stripped_line.endswith("/")
+                and len(line) - len(line.lstrip())
+                <= 4  # Minimal indentation (0-4 spaces)
+                and not any(char in line for char in ["├", "└", "│", "─"])
+            ):  # No tree characters
+                root_directory = stripped_line.rstrip("/")
+                path_stack = [root_directory]
+                continue
+
+            # Only process lines that have tree structure
+            if not any(char in line for char in ["├", "└", "│", "─"]):
+                continue
+
+            # Parse tree structure depth by analyzing the line structure
+            # Count │ characters before the actual item, or use indentation as fallback
+            pipe_count = 0
+
+            for i, char in enumerate(line):
+                if char == "│":
+                    pipe_count += 1
+                elif char in ["├", "└"]:
+                    break
+
+            # Calculate depth: use pipe count if available, otherwise use indentation
+            if pipe_count > 0:
+                depth = pipe_count + 1  # +1 because the actual item is one level deeper
+            else:
+                # Use indentation to determine depth (every 4 spaces = 1 level)
+                indent_spaces = len(line) - len(line.lstrip())
+                depth = max(1, indent_spaces // 4)  # At least depth 1
+
+            # Clean the line to get the item name
+            clean_line = line
+            for char in ["├──", "└──", "├", "└", "│", "─"]:
+                clean_line = clean_line.replace(char, "")
+            clean_line = clean_line.strip()
+
+            if not clean_line or ":" in clean_line:
+                continue
+
+            # Extract filename (remove comments)
+            if "#" in clean_line:
+                filename = clean_line.split("#")[0].strip()
+            else:
+                filename = clean_line.strip()
+
+            # Skip empty filenames
+            if not filename:
+                continue
+
+            # Adjust path stack to current depth
+            while len(path_stack) < depth:
+                path_stack.append("")
+            path_stack = path_stack[:depth]
+
+            # Determine if it's a directory or file
+            is_directory = (
+                filename.endswith("/")
+                or (
+                    "." not in filename
+                    and filename not in ["README", "requirements.txt", "setup.py"]
+                )
+                or filename
+                in [
+                    "core",
+                    "networks",
+                    "environments",
+                    "baselines",
+                    "evaluation",
+                    "experiments",
+                    "utils",
+                    "src",
+                    "lib",
+                    "app",
+                ]
+            )
+
+            if is_directory:
+                directory_name = filename.rstrip("/")
+                if directory_name and ":" not in directory_name:
+                    path_stack.append(directory_name)
+            else:
+                # It's a file, construct full path
+                if path_stack:
+                    full_path = "/".join(path_stack) + "/" + filename
+                else:
+                    full_path = filename
+                files.append(full_path)
+
+        return files
+
+    def _extract_from_simple_list(self, lines: List[str]) -> List[str]:
+        """Extract files from simple list format (- filename)"""
+        files = []
+
+        for line in lines:
+            line = line.strip()
+            if line.startswith("- ") and not line.startswith('- "'):
+                # Remove leading "- " and clean up
+                filename = line[2:].strip()
+
+                # Remove quotes if present
+                if filename.startswith('"') and filename.endswith('"'):
+                    filename = filename[1:-1]
+
+                # Check if it looks like a file (has extension)
+                if "." in filename and "/" in filename:
+                    files.append(filename)
+
+        return files
+
+    def _extract_from_plan_content(self, lines: List[str]) -> List[str]:
+        """Extract files from anywhere in the plan content"""
+        files = []
+
+        # Look for common file patterns
+        import re
+
+        file_patterns = [
+            r"([a-zA-Z0-9_\-/]+\.[a-zA-Z0-9]+)",  # filename.ext
+            r'"([a-zA-Z0-9_\-/]+\.[a-zA-Z0-9]+)"',  # "filename.ext"
+        ]
+
+        for line in lines:
+            for pattern in file_patterns:
+                matches = re.findall(pattern, line)
+                for match in matches:
+                    # Only include if it looks like a code file (exclude media files)
+                    if "/" in match and any(
+                        ext in match
+                        for ext in [
+                            ".py",
+                            ".js",
+                            ".html",
+                            ".css",
+                            ".md",
+                            ".txt",
+                            ".json",
+                            ".yaml",
+                            ".yml",
+                            ".xml",
+                            ".sql",
+                            ".sh",
+                            ".ts",
+                            ".jsx",
+                            ".tsx",
+                        ]
+                    ):
+                        files.append(match)
+
+        return files
+
+    def _clean_and_validate_files(self, files: List[str]) -> List[str]:
+        """Clean and validate extracted file paths - only keep code files"""
+        cleaned_files = []
+
+        # Define code file extensions we want to track
+        code_extensions = [
+            ".py",
+            ".js",
+            ".html",
+            ".css",
+            ".md",
+            ".txt",
+            ".json",
+            ".yaml",
+            ".yml",
+            ".xml",
+            ".sql",
+            ".sh",
+            ".bat",
+            ".dockerfile",
+            ".env",
+            ".gitignore",
+            ".ts",
+            ".jsx",
+            ".tsx",
+            ".vue",
+            ".php",
+            ".rb",
+            ".go",
+            ".rs",
+            ".cpp",
+            ".c",
+            ".h",
+            ".hpp",
+            ".java",
+            ".kt",
+            ".swift",
+            ".dart",
+        ]
+
+        for file_path in files:
+            # Clean the path
+            cleaned_path = file_path.strip().strip('"').strip("'")
+
+            # Skip if empty
+            if not cleaned_path:
+                continue
+
+            # Skip directories (no file extension)
+            if "." not in cleaned_path.split("/")[-1]:
+                continue
+
+            # Only include files with code extensions
+            has_code_extension = any(
+                cleaned_path.lower().endswith(ext) for ext in code_extensions
+            )
+            if not has_code_extension:
+                continue
+
+            # Skip files that look like YAML keys or config entries
+            if (
+                ":" in cleaned_path
+                and not cleaned_path.endswith(".yaml")
+                and not cleaned_path.endswith(".yml")
+            ):
+                continue
+
+            # Skip paths that contain invalid characters for file paths
+            if any(invalid_char in cleaned_path for invalid_char in ['"', "'", "|"]):
+                continue
+
+            # Add to cleaned list if not already present
+            if cleaned_path not in cleaned_files:
+                cleaned_files.append(cleaned_path)
+
+        return sorted(cleaned_files)
+
+    def record_file_implementation(
+        self, file_path: str, implementation_content: str = ""
+    ):
+        """
+        Record a newly implemented file (simplified version)
+        NEW LOGIC: File implementation is tracked via write_file tool detection
+
+        Args:
+            file_path: Path of the implemented file
+            implementation_content: Content of the implemented file
+        """
+        # Add file to implemented files list if not already present
+        if file_path not in self.implemented_files:
+            self.implemented_files.append(file_path)
+
+        self.logger.info(f"📝 File implementation recorded: {file_path}")
+
+    async def create_code_implementation_summary(
+        self,
+        client,
+        client_type: str,
+        file_path: str,
+        implementation_content: str,
+        files_implemented: int,
+    ) -> str:
+        """
+        Create LLM-based code implementation summary after writing a file
+        Uses LLM to analyze and summarize the implemented code
+
+        Args:
+            client: LLM client instance
+            client_type: Type of LLM client ("anthropic" or "openai")
+            file_path: Path of the implemented file
+            implementation_content: Content of the implemented file
+            files_implemented: Number of files implemented so far
+
+        Returns:
+            LLM-generated formatted code implementation summary
+        """
+        try:
+            # Record the file implementation first
+            self.record_file_implementation(file_path, implementation_content)
+
+            # Create prompt for LLM summary
+            summary_prompt = self._create_code_summary_prompt(
+                file_path, implementation_content, files_implemented
+            )
+            summary_messages = [{"role": "user", "content": summary_prompt}]
+
+            # Get LLM-generated summary
+            llm_response = await self._call_llm_for_summary(
+                client, client_type, summary_messages
+            )
+            llm_summary = llm_response.get("content", "")
+
+            # Extract different sections from LLM summary
+            sections = self._extract_summary_sections(llm_summary)
+
+            # Store Next Steps in temporary variable (not saved to file)
+            self.current_next_steps = sections.get("next_steps", "")
+            if self.current_next_steps:
+                self.logger.info("📝 Next Steps stored temporarily (not saved to file)")
+
+            # Format summary with only Implementation Progress and Dependencies for file saving
+            file_summary_content = ""
+            if sections.get("core_purpose"):
+                file_summary_content += sections["core_purpose"] + "\n\n"
+            if sections.get("public_interface"):
+                file_summary_content += sections["public_interface"] + "\n\n"
+            if sections.get("internal_dependencies"):
+                file_summary_content += sections["internal_dependencies"] + "\n\n"
+            if sections.get("external_dependencies"):
+                file_summary_content += sections["external_dependencies"] + "\n\n"
+            if sections.get("implementation_notes"):
+                file_summary_content += sections["implementation_notes"] + "\n\n"
+
+            # Create the formatted summary for file saving (without Next Steps)
+            formatted_summary = self._format_code_implementation_summary(
+                file_path, file_summary_content.strip(), files_implemented
+            )
+
+            # Save to implement_code_summary.md (append mode) - only Implementation Progress and Dependencies
+            await self._save_code_summary_to_file(formatted_summary, file_path)
+
+            self.logger.info(f"Created and saved code summary for: {file_path}")
+            return formatted_summary
+
+        except Exception as e:
+            self.logger.error(
+                f"Failed to create LLM-based code implementation summary: {e}"
+            )
+            # Fallback to simple summary
+            return self._create_fallback_code_summary(
+                file_path, implementation_content, files_implemented
+            )
+
+    def _create_code_summary_prompt(
+        self, file_path: str, implementation_content: str, files_implemented: int
+    ) -> str:
+        """
+        Create prompt for LLM to generate code implementation summary
+
+        Args:
+            file_path: Path of the implemented file
+            implementation_content: Content of the implemented file
+            files_implemented: Number of files implemented so far
+
+        Returns:
+            Prompt for LLM summarization
+        """
+        current_round = self.current_round
+
+        # Get formatted file lists
+        file_lists = self.get_formatted_files_lists()
+        implemented_files_list = file_lists["implemented"]
+        unimplemented_files_list = file_lists["unimplemented"]
+
+        prompt = f"""You are an expert code implementation summarizer. Analyze the implemented code file and create a structured summary.
+
+**🚨 CRITICAL: The files listed below are ALREADY IMPLEMENTED - DO NOT suggest them in Next Steps! 🚨**
+
+**All Previously Implemented Files:**
+{implemented_files_list}
+
+**Remaining Unimplemented Files (choose ONLY from these for Next Steps):**
+{unimplemented_files_list}
+
+**Current Implementation Context:**
+- **File Implemented**: {file_path}
+- **Current Round**: {current_round}
+- **Total Files Implemented**: {files_implemented}
+
+
+**Initial Plan Reference:**
+{self.initial_plan[:]}
+
+**Implemented Code Content:**
+```
+{implementation_content[:]}
+```
+
+**Required Summary Format:**
+
+**Core Purpose** (provide a general overview of the file's main responsibility):
+- {{1-2 sentence description of file's main responsibility}}
+
+**Public Interface** (what other files can use, if any):
+- Class {{ClassName}}: {{purpose}} | Key methods: {{method_names}} | Constructor params: {{params}}
+- Function {{function_name}}({{params}}): {{purpose}} -> {{return_type}}: {{purpose}}
+- Constants/Types: {{name}}: {{value/description}}
+
+**Internal Dependencies** (what this file imports/requires, if any):
+- From {{module/file}}: {{specific_imports}}
+- External packages: {{package_name}} - {{usage_context}}
+
+**External Dependencies** (what depends on this file, if any):
+- Expected to be imported by: {{likely_consumer_files}}
+- Key exports used elsewhere: {{main_interfaces}}
+
+**Implementation Notes**: (if any)
+- Architecture decisions: {{key_choices_made}}
+- Cross-File Relationships: {{how_files_work_together}}
+
+**Next Steps**: List the code file (ONLY ONE) that will be implemented in the next round (MUST choose from "Remaining Unimplemented Files" above)
+  Format: Code will be implemented: {{file_path}}
+  **NEVER suggest any file from the "All Previously Implemented Files" list!**
+
+**Instructions:**
+- Be precise and concise
+- Focus on function interfaces that other files will need
+- Extract actual function signatures from the code
+- **CRITICAL: For Next Steps, ONLY choose ONE file from the "Remaining Unimplemented Files" list above**
+- **NEVER suggest implementing a file that is already in the implemented files list**
+- Choose the next file based on logical dependencies and implementation order
+- Use the exact format specified above
+
+**Summary:**"""
+
+        return prompt
+
+    # TODO: The prompt is not good, need to be improved
+    # **Implementation Progress**: List the code file completed in current round and core implementation ideas
+    #   Format: {{file_path}}: {{core implementation ideas}}
+
+    # **Dependencies**: According to the File Structure and initial plan, list functions that may be called by other files
+    #   Format: {{file_path}}: Function {{function_name}}: core ideas--{{ideas}}; Required parameters--{{params}}; Return parameters--{{returns}}
+    #   Required packages: {{packages}}
+
+    def _extract_summary_sections(self, llm_summary: str) -> Dict[str, str]:
+        """
+        Extract different sections from LLM-generated summary
+
+        Args:
+            llm_summary: Raw LLM-generated summary text
+
+        Returns:
+            Dictionary with extracted sections: core_purpose, public_interface, internal_dependencies,
+            external_dependencies, implementation_notes, next_steps
+        """
+        sections = {
+            "core_purpose": "",
+            "public_interface": "",
+            "internal_dependencies": "",
+            "external_dependencies": "",
+            "implementation_notes": "",
+            "next_steps": "",
+        }
+
+        try:
+            lines = llm_summary.split("\n")
+            current_section = None
+            current_content = []
+
+            for line in lines:
+                line_lower = line.lower().strip()
+
+                # Check for section headers
+                if "core purpose" in line_lower:
+                    if current_section and current_content:
+                        sections[current_section] = "\n".join(current_content).strip()
+                    current_section = "core_purpose"
+                    current_content = [line]  # Include the header
+                elif "public interface" in line_lower:
+                    if current_section and current_content:
+                        sections[current_section] = "\n".join(current_content).strip()
+                    current_section = "public_interface"
+                    current_content = [line]  # Include the header
+                elif "internal dependencies" in line_lower:
+                    if current_section and current_content:
+                        sections[current_section] = "\n".join(current_content).strip()
+                    current_section = "internal_dependencies"
+                    current_content = [line]  # Include the header
+                elif "external dependencies" in line_lower:
+                    if current_section and current_content:
+                        sections[current_section] = "\n".join(current_content).strip()
+                    current_section = "external_dependencies"
+                    current_content = [line]  # Include the header
+                elif "implementation notes" in line_lower:
+                    if current_section and current_content:
+                        sections[current_section] = "\n".join(current_content).strip()
+                    current_section = "implementation_notes"
+                    current_content = [line]  # Include the header
+                elif "next steps" in line_lower:
+                    if current_section and current_content:
+                        sections[current_section] = "\n".join(current_content).strip()
+                    current_section = "next_steps"
+                    current_content = [line]  # Include the header
+                else:
+                    # Add content to current section
+                    if current_section:
+                        current_content.append(line)
+
+            # Don't forget the last section
+            if current_section and current_content:
+                sections[current_section] = "\n".join(current_content).strip()
+
+            self.logger.info(f"📋 Extracted sections: {list(sections.keys())}")
+
+        except Exception as e:
+            self.logger.error(f"Failed to extract summary sections: {e}")
+            # Fallback: put everything in core_purpose
+            sections["core_purpose"] = llm_summary
+
+        return sections
+
+    def _format_code_implementation_summary(
+        self, file_path: str, llm_summary: str, files_implemented: int
+    ) -> str:
+        """
+        Format the LLM-generated summary into the final structure
+
+        Args:
+            file_path: Path of the implemented file
+            llm_summary: LLM-generated summary content
+            files_implemented: Number of files implemented so far
+
+        Returns:
+            Formatted summary
+        """
+        timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+
+        # # Create formatted list of implemented files
+        # implemented_files_list = (
+        #     "\n".join([f"- {file}" for file in self.implemented_files])
+        #     if self.implemented_files
+        #     else "- None yet"
+        # )
+
+        #         formatted_summary = f"""# Code Implementation Summary
+        # **All Previously Implemented Files:**
+        # {implemented_files_list}
+        # **Generated**: {timestamp}
+        # **File Implemented**: {file_path}
+        # **Total Files Implemented**: {files_implemented}
+
+        # {llm_summary}
+
+        # ---
+        # *Auto-generated by Memory Agent*
+        # """
+        formatted_summary = f"""# Code Implementation Summary
+**Generated**: {timestamp}
+**File Implemented**: {file_path}
+
+{llm_summary}
+
+---
+*Auto-generated by Memory Agent*
+"""
+        return formatted_summary
+
+    def _create_fallback_code_summary(
+        self, file_path: str, implementation_content: str, files_implemented: int
+    ) -> str:
+        """
+        Create fallback summary when LLM is unavailable
+
+        Args:
+            file_path: Path of the implemented file
+            implementation_content: Content of the implemented file
+            files_implemented: Number of files implemented so far
+
+        Returns:
+            Fallback summary
+        """
+        # Create formatted list of implemented files
+        implemented_files_list = (
+            "\n".join([f"- {file}" for file in self.implemented_files])
+            if self.implemented_files
+            else "- None yet"
+        )
+
+        summary = f"""# Code Implementation Summary
+**All Previously Implemented Files:**
+{implemented_files_list}
+**Generated**: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}
+**File Implemented**: {file_path}
+**Total Files Implemented**: {files_implemented}
+**Summary failed to generate.**
+
+---
+*Auto-generated by Concise Memory Agent (Fallback Mode)*
+"""
+        return summary
+
+    async def _save_code_summary_to_file(self, new_summary: str, file_path: str):
+        """
+        Append code implementation summary to implement_code_summary.md
+        Accumulates all implementations with clear separators
+
+        Args:
+            new_summary: New summary content to append
+            file_path: Path of the file for which the summary was generated
+        """
+        try:
+            # Create directory if it doesn't exist
+            os.makedirs(os.path.dirname(self.code_summary_path), exist_ok=True)
+
+            # Check if file exists to determine if we need header
+            file_exists = os.path.exists(self.code_summary_path)
+
+            # Open in append mode to accumulate all implementations
+            with open(self.code_summary_path, "a", encoding="utf-8") as f:
+                if not file_exists:
+                    # Write header for new file
+                    f.write("# Code Implementation Progress Summary\n")
+                    f.write("*Accumulated implementation progress for all files*\n\n")
+
+                # Add clear separator between implementations
+                f.write("\n" + "=" * 80 + "\n")
+                f.write(
+                    f"## IMPLEMENTATION File {file_path}; ROUND {self.current_round} \n"
+                )
+                f.write("=" * 80 + "\n\n")
+
+                # Write the new summary
+                f.write(new_summary)
+                f.write("\n\n")
+
+            self.logger.info(
+                f"Appended LLM-based code implementation summary to: {self.code_summary_path}"
+            )
+
+        except Exception as e:
+            self.logger.error(f"Failed to save code implementation summary: {e}")
+
+    async def _call_llm_for_summary(
+        self, client, client_type: str, summary_messages: List[Dict]
+    ) -> Dict[str, Any]:
+        """
+        Call LLM for code implementation summary generation ONLY
+
+        This method is used only for creating code implementation summaries,
+        NOT for conversation summarization which has been removed.
+        """
+        if client_type == "anthropic":
+            response = await client.messages.create(
+                model=self.default_models["anthropic"],
+                system="You are an expert code implementation summarizer. Create structured summaries of implemented code files that preserve essential information about functions, dependencies, and implementation approaches.",
+                messages=summary_messages,
+                max_tokens=5000,
+                temperature=0.2,
+            )
+
+            content = ""
+            for block in response.content:
+                if block.type == "text":
+                    content += block.text
+
+            return {"content": content}
+
+        elif client_type == "openai":
+            openai_messages = [
+                {
+                    "role": "system",
+                    "content": "You are an expert code implementation summarizer. Create structured summaries of implemented code files that preserve essential information about functions, dependencies, and implementation approaches.",
+                }
+            ]
+            openai_messages.extend(summary_messages)
+
+            # Try max_tokens and temperature first, fallback to max_completion_tokens without temperature if unsupported
+            try:
+                response = await client.chat.completions.create(
+                    model=self.default_models["openai"],
+                    messages=openai_messages,
+                    max_tokens=5000,
+                    temperature=0.2,
+                )
+            except Exception as e:
+                if "max_tokens" in str(e) and "max_completion_tokens" in str(e):
+                    # Retry with max_completion_tokens and no temperature for models that require it
+                    response = await client.chat.completions.create(
+                        model=self.default_models["openai"],
+                        messages=openai_messages,
+                        max_completion_tokens=5000,
+                    )
+                else:
+                    raise
+
+            return {"content": response.choices[0].message.content or ""}
+
+        else:
+            raise ValueError(f"Unsupported client type: {client_type}")
+
+    def start_new_round(self, iteration: Optional[int] = None):
+        """Start a new dialogue round and reset tool results
+
+        Args:
+            iteration: Optional iteration number from workflow to sync with current_round
+        """
+        if iteration is not None:
+            # Sync with workflow iteration
+            self.current_round = iteration
+            # self.logger.info(f"🔄 Synced round with workflow iteration {iteration}")
+        else:
+            # Default behavior: increment round counter
+            self.current_round += 1
+            self.logger.info(f"🔄 Started new round {self.current_round}")
+
+        self.current_round_tool_results = []  # Clear previous round results
+        # Note: Don't reset last_write_file_detected and should_clear_memory_next here
+        # These flags persist across rounds until memory optimization is applied
+        # self.logger.info(f"🔄 Round {self.current_round} - Tool results cleared, memory flags preserved")
+
+    def record_tool_result(
+        self, tool_name: str, tool_input: Dict[str, Any], tool_result: Any
+    ):
+        """
+        Record tool result for current round and detect write_file calls
+
+        Args:
+            tool_name: Name of the tool called
+            tool_input: Input parameters for the tool
+            tool_result: Result returned by the tool
+        """
+        # Detect write_file calls to trigger memory clearing
+        if tool_name == "write_file":
+            self.last_write_file_detected = True
+            self.should_clear_memory_next = True
+
+            # self.logger.info(f"🔄 WRITE_FILE DETECTED: {file_path} - Memory will be cleared in next round")
+
+        # Only record specific tools that provide essential information
+        essential_tools = [
+            "read_code_mem",  # Read code summary from implement_code_summary.md
+            "read_file",  # Read file contents
+            "write_file",  # Write file contents (important for tracking implementations)
+            "execute_python",  # Execute Python code (for testing/validation)
+            "execute_bash",  # Execute bash commands (for build/execution)
+            "search_code",  # Search code patterns
+            "search_reference_code",  # Search reference code (if available)
+            "get_file_structure",  # Get file structure (for understanding project layout)
+        ]
+
+        if tool_name in essential_tools:
+            tool_record = {
+                "tool_name": tool_name,
+                "tool_input": tool_input,
+                "tool_result": tool_result,
+                "timestamp": time.time(),
+            }
+            self.current_round_tool_results.append(tool_record)
+            # self.logger.info(f"📊 Essential tool result recorded: {tool_name} ({len(self.current_round_tool_results)} total)")
+
+    def should_use_concise_mode(self) -> bool:
+        """
+        Check if concise memory mode should be used
+
+        Returns:
+            True if first file has been generated and concise mode should be active
+        """
+        return self.last_write_file_detected
+
+    def create_concise_messages(
+        self,
+        system_prompt: str,
+        messages: List[Dict[str, Any]],
+        files_implemented: int,
+    ) -> List[Dict[str, Any]]:
+        """
+        Create concise message list for LLM input
+        NEW LOGIC: Always clear after write_file, keep system_prompt + initial_plan + current round tools
+
+        Args:
+            system_prompt: Current system prompt
+            messages: Original message list
+            files_implemented: Number of files implemented so far
+
+        Returns:
+            Concise message list containing only essential information
+        """
+        if not self.last_write_file_detected:
+            # Before any write_file, use normal flow
+            self.logger.info(
+                "🔄 Using normal conversation flow (before any write_file)"
+            )
+            return messages
+
+        # After write_file detection, use concise approach with clean slate
+        self.logger.info(
+            f"🎯 Using CONCISE memory mode - Clear slate after write_file, Round {self.current_round}"
+        )
+
+        concise_messages = []
+
+        # Get formatted file lists
+        file_lists = self.get_formatted_files_lists()
+        implemented_files_list = file_lists["implemented"]
+
+        # 1. Add initial plan message (always preserved)
+        initial_plan_message = {
+            "role": "user",
+            "content": f"""**Task: Implement code based on the following reproduction plan**
+
+**Code Reproduction Plan:**
+{self.initial_plan}
+
+**Working Directory:** Current workspace
+
+**All Previously Implemented Files:**
+{implemented_files_list}
+
+**Current Status:** {files_implemented} files implemented
+
+**Objective:** Continue implementation by analyzing dependencies and implementing the next required file according to the plan's priority order.""",
+        }
+
+        # Append Next Steps information if available
+        if self.current_next_steps.strip():
+            initial_plan_message["content"] += (
+                f"\n\n**Next Steps (from previous analysis):**\n{self.current_next_steps}"
+            )
+
+        # Debug output for unimplemented files (clean format without dashes)
+        unimplemented_files = self.get_unimplemented_files()
+        print("✅ Unimplemented Files:")
+        for file_path in unimplemented_files:
+            print(f"{file_path}")
+        if self.current_next_steps.strip():
+            print(f"\n📋 {self.current_next_steps}")
+
+        concise_messages.append(initial_plan_message)
+
+        # 2. Add Knowledge Base
+        knowledge_base_message = {
+            "role": "user",
+            "content": f"""**Below is the Knowledge Base of the LATEST implemented code file:**
+{self._read_code_knowledge_base()}
+
+**Development Cycle - START HERE:**
+
+**For NEW file implementation:**
+1. **You need to call read_code_mem(already_implemented_file_path)** to understand existing implementations and dependencies - agent should choose relevant ALREADY IMPLEMENTED file paths for reference, NOT the new file you want to create
+2. Write_file can be used to implement the new component
+3. Finally: Use execute_python or execute_bash for testing (if needed)
+
+**When all files implemented:**
+**Use execute_python or execute_bash** to test the complete implementation""",
+        }
+        concise_messages.append(knowledge_base_message)
+
+        # 3. Add current tool results (essential information for next file generation)
+        if self.current_round_tool_results:
+            tool_results_content = self._format_tool_results()
+
+            # # Append Next Steps information if available
+            # if self.current_next_steps.strip():
+            #     tool_results_content += f"\n\n**Next Steps (from previous analysis):**\n{self.current_next_steps}"
+
+            tool_results_message = {
+                "role": "user",
+                "content": f"""**Current Tool Results:**
+{tool_results_content}""",
+            }
+            concise_messages.append(tool_results_message)
+        else:
+            # If no tool results yet, add guidance for next steps
+            guidance_content = f"""**Current Round:** {self.current_round}
+
+**Development Cycle - START HERE:**
+
+**For NEW file implementation:**
+1. **You need to call read_code_mem(already_implemented_file_path)** to understand existing implementations and dependencies - agent should choose relevant ALREADY IMPLEMENTED file paths for reference, NOT the new file you want to create
+2. Write_file can be used to implement the new component
+3. Finally: Use execute_python or execute_bash for testing (if needed)
+
+**When all files implemented:**
+1. **Use execute_python or execute_bash** to test the complete implementation"""
+
+            # # Append Next Steps information if available (even when no tool results)
+            # if self.current_next_steps.strip():
+            #     guidance_content += f"\n\n**Next Steps (from previous analysis):**\n{self.current_next_steps}"
+
+            guidance_message = {
+                "role": "user",
+                "content": guidance_content,
+            }
+            concise_messages.append(guidance_message)
+        # **Available Essential Tools:** read_code_mem, write_file, execute_python, execute_bash
+        # **Remember:** Start with read_code_mem when implementing NEW files to understand existing code. When all files are implemented, focus on testing and completion. Implement according to the original paper's specifications - any reference code is for inspiration only."""
+        # self.logger.info(f"✅ Concise messages created: {len(concise_messages)} messages (original: {len(messages)})")
+        return concise_messages
+
+    def _read_code_knowledge_base(self) -> Optional[str]:
+        """
+        Read the implement_code_summary.md file as code knowledge base
+        Returns only the final/latest implementation entry, not all historical entries
+
+        Returns:
+            Content of the latest implementation entry if it exists, None otherwise
+        """
+        try:
+            if os.path.exists(self.code_summary_path):
+                with open(self.code_summary_path, "r", encoding="utf-8") as f:
+                    content = f.read().strip()
+
+                if content:
+                    # Extract only the final/latest implementation entry
+                    return self._extract_latest_implementation_entry(content)
+                else:
+                    return None
+            else:
+                return None
+
+        except Exception as e:
+            self.logger.error(f"Failed to read code knowledge base: {e}")
+            return None
+
+    def _extract_latest_implementation_entry(self, content: str) -> Optional[str]:
+        """
+        Extract the latest/final implementation entry from the implement_code_summary.md content
+        Uses a simpler approach to find the last implementation section
+
+        Args:
+            content: Full content of implement_code_summary.md
+
+        Returns:
+            Latest implementation entry content, or None if not found
+        """
+        try:
+            import re
+
+            # Pattern to match the start of implementation sections
+            section_pattern = (
+                r"={80}\s*\n## IMPLEMENTATION File .+?; ROUND \d+\s*\n={80}"
+            )
+
+            # Find all implementation section starts
+            matches = list(re.finditer(section_pattern, content))
+
+            if not matches:
+                # No implementation sections found
+                lines = content.split("\n")
+                fallback_content = (
+                    "\n".join(lines[:10]) + "\n... (truncated for brevity)"
+                    if len(lines) > 10
+                    else content
+                )
+                self.logger.info(
+                    "📖 No implementation sections found, using fallback content"
+                )
+                return fallback_content
+
+            # Get the start position of the last implementation section
+            last_match = matches[-1]
+            start_pos = last_match.start()
+
+            # Take everything from the last section start to the end of content
+            latest_entry = content[start_pos:].strip()
+
+            # self.logger.info(f"📖 Extracted latest implementation entry from knowledge base")
+            # print(f"DEBUG: Extracted content length: {len(latest_entry)}")
+            # print(f"DEBUG: First 200 chars: {latest_entry[:]}")
+
+            return latest_entry
+
+        except Exception as e:
+            self.logger.error(f"Failed to extract latest implementation entry: {e}")
+            # Return last 1000 characters as fallback
+            return content[-500:] if len(content) > 500 else content
+
+    def _format_tool_results(self) -> str:
+        """
+        Format current round tool results for LLM input
+
+        Returns:
+            Formatted string of tool results
+        """
+        if not self.current_round_tool_results:
+            return "No tool results in current round."
+
+        formatted_results = []
+
+        for result in self.current_round_tool_results:
+            tool_name = result["tool_name"]
+            tool_input = result["tool_input"]
+            tool_result = result["tool_result"]
+
+            # Format based on tool type
+            if tool_name == "read_code_mem":
+                file_path = tool_input.get("file_path", "unknown")
+                formatted_results.append(f"""
+**read_code_mem Result for {file_path}:**
+{self._format_tool_result_content(tool_result)}
+""")
+            elif tool_name == "read_file":
+                file_path = tool_input.get("file_path", "unknown")
+                formatted_results.append(f"""
+**read_file Result for {file_path}:**
+{self._format_tool_result_content(tool_result)}
+""")
+            elif tool_name == "write_file":
+                file_path = tool_input.get("file_path", "unknown")
+                formatted_results.append(f"""
+**write_file Result for {file_path}:**
+{self._format_tool_result_content(tool_result)}
+""")
+            elif tool_name == "execute_python":
+                code_snippet = (
+                    tool_input.get("code", "")[:50] + "..."
+                    if len(tool_input.get("code", "")) > 50
+                    else tool_input.get("code", "")
+                )
+                formatted_results.append(f"""
+**execute_python Result (code: {code_snippet}):**
+{self._format_tool_result_content(tool_result)}
+""")
+            elif tool_name == "execute_bash":
+                command = tool_input.get("command", "unknown")
+                formatted_results.append(f"""
+**execute_bash Result (command: {command}):**
+{self._format_tool_result_content(tool_result)}
+""")
+            elif tool_name == "search_code":
+                pattern = tool_input.get("pattern", "unknown")
+                file_pattern = tool_input.get("file_pattern", "")
+                formatted_results.append(f"""
+**search_code Result (pattern: {pattern}, files: {file_pattern}):**
+{self._format_tool_result_content(tool_result)}
+""")
+            elif tool_name == "search_reference_code":
+                target_file = tool_input.get("target_file", "unknown")
+                keywords = tool_input.get("keywords", "")
+                formatted_results.append(f"""
+**search_reference_code Result for {target_file} (keywords: {keywords}):**
+{self._format_tool_result_content(tool_result)}
+""")
+            elif tool_name == "get_file_structure":
+                directory = tool_input.get(
+                    "directory_path", tool_input.get("path", "current")
+                )
+                formatted_results.append(f"""
+**get_file_structure Result for {directory}:**
+{self._format_tool_result_content(tool_result)}
+""")
+
+        return "\n".join(formatted_results)
+
+    def _format_tool_result_content(self, tool_result: Any) -> str:
+        """
+        Format tool result content for display
+
+        Args:
+            tool_result: Tool result to format
+
+        Returns:
+            Formatted string representation
+        """
+        if isinstance(tool_result, str):
+            # Try to parse as JSON for better formatting
+            try:
+                result_data = json.loads(tool_result)
+                if isinstance(result_data, dict):
+                    # Format key information
+                    if result_data.get("status") == "summary_found":
+                        return (
+                            f"Summary found:\n{result_data.get('summary_content', '')}"
+                        )
+                    elif result_data.get("status") == "no_summary":
+                        return "No summary available"
+                    else:
+                        return json.dumps(result_data, indent=2)
+                else:
+                    return str(result_data)
+            except json.JSONDecodeError:
+                return tool_result
+        else:
+            return str(tool_result)
+
+    def get_memory_statistics(self, files_implemented: int = 0) -> Dict[str, Any]:
+        """Get memory agent statistics"""
+        unimplemented_files = self.get_unimplemented_files()
+        return {
+            "last_write_file_detected": self.last_write_file_detected,
+            "should_clear_memory_next": self.should_clear_memory_next,
+            "current_round": self.current_round,
+            "concise_mode_active": self.should_use_concise_mode(),
+            "current_round_tool_results": len(self.current_round_tool_results),
+            "essential_tools_recorded": [
+                r["tool_name"] for r in self.current_round_tool_results
+            ],
+            "implemented_files_tracked": files_implemented,
+            "implemented_files_list": self.implemented_files.copy(),
+            "phases_parsed": len(self.phase_structure),
+            "next_steps_available": bool(self.current_next_steps.strip()),
+            "next_steps_length": len(self.current_next_steps.strip())
+            if self.current_next_steps
+            else 0,
+            # File tracking statistics
+            "total_files_in_plan": len(self.all_files_list),
+            "files_implemented_count": len(self.implemented_files),
+            "files_remaining_count": len(unimplemented_files),
+            "all_files_list": self.all_files_list.copy(),
+            "unimplemented_files_list": unimplemented_files,
+            "implementation_progress_percent": (
+                len(self.implemented_files) / len(self.all_files_list) * 100
+            )
+            if self.all_files_list
+            else 0,
+        }
+
+    def get_implemented_files(self) -> List[str]:
+        """Get list of all implemented files"""
+        return self.implemented_files.copy()
+
+    def get_all_files_list(self) -> List[str]:
+        """Get list of all files that should be implemented according to the plan"""
+        return self.all_files_list.copy()
+
+    def get_unimplemented_files(self) -> List[str]:
+        """
+        Get list of files that haven't been implemented yet
+
+        Returns:
+            List of file paths that still need to be implemented
+        """
+        implemented_set = set(self.implemented_files)
+        unimplemented = [f for f in self.all_files_list if f not in implemented_set]
+        return unimplemented
+
+    def get_formatted_files_lists(self) -> Dict[str, str]:
+        """
+        Get formatted strings for implemented and unimplemented files
+
+        Returns:
+            Dictionary with 'implemented' and 'unimplemented' formatted lists
+        """
+        implemented_list = (
+            "\n".join([f"- {file}" for file in self.implemented_files])
+            if self.implemented_files
+            else "- None yet"
+        )
+
+        unimplemented_files = self.get_unimplemented_files()
+        unimplemented_list = (
+            "\n".join([f"- {file}" for file in unimplemented_files])
+            if unimplemented_files
+            else "- All files implemented!"
+        )
+
+        return {"implemented": implemented_list, "unimplemented": unimplemented_list}
+
+    def get_current_next_steps(self) -> str:
+        """Get the current Next Steps information"""
+        return self.current_next_steps
+
+    def clear_next_steps(self):
+        """Clear the stored Next Steps information"""
+        if self.current_next_steps.strip():
+            self.logger.info("🧹 Next Steps information cleared")
+        self.current_next_steps = ""
+
+    def set_next_steps(self, next_steps: str):
+        """Manually set Next Steps information"""
+        self.current_next_steps = next_steps
+        self.logger.info(
+            f"📝 Next Steps manually set ({len(next_steps.strip())} chars)"
+        )
+
+    def should_trigger_memory_optimization(
+        self, messages: List[Dict[str, Any]], files_implemented: int = 0
+    ) -> bool:
+        """
+        Check if memory optimization should be triggered
+        NEW LOGIC: Trigger after write_file has been detected
+
+        Args:
+            messages: Current message list
+            files_implemented: Number of files implemented so far
+
+        Returns:
+            True if concise mode should be applied
+        """
+        # Trigger if we detected write_file and should clear memory
+        if self.should_clear_memory_next:
+            # self.logger.info(f"🎯 Triggering CONCISE memory optimization (write_file detected, files: {files_implemented})")
+            return True
+
+        # No optimization before any write_file
+        return False
+
+    def apply_memory_optimization(
+        self, system_prompt: str, messages: List[Dict[str, Any]], files_implemented: int
+    ) -> List[Dict[str, Any]]:
+        """
+        Apply memory optimization using concise approach
+        NEW LOGIC: Clear all history after write_file, keep only system_prompt + initial_plan + current tools
+
+        Args:
+            system_prompt: Current system prompt
+            messages: Original message list
+            files_implemented: Number of files implemented so far
+
+        Returns:
+            Optimized message list
+        """
+        if not self.should_clear_memory_next:
+            # Before any write_file, return original messages
+            return messages
+
+        # Apply concise memory optimization after write_file detection
+        # self.logger.info(f"🧹 CLEARING MEMORY after write_file - creating clean slate")
+        optimized_messages = self.create_concise_messages(
+            system_prompt, messages, files_implemented
+        )
+
+        # Clear the flag after applying optimization
+        self.should_clear_memory_next = False
+
+        compression_ratio = (
+            ((len(messages) - len(optimized_messages)) / len(messages) * 100)
+            if messages
+            else 0
+        )
+        self.logger.info(
+            f"🎯 CONCISE optimization applied: {len(messages)} → {len(optimized_messages)} messages ({compression_ratio:.1f}% compression)"
+        )
+
+        return optimized_messages
+
+    def clear_current_round_tool_results(self):
+        """Clear current round tool results (called when starting new round)"""
+        self.current_round_tool_results = []
+        self.logger.info("🧹 Current round tool results cleared")
+
+    def debug_concise_state(self, files_implemented: int = 0):
+        """Debug method to show current concise memory state"""
+        stats = self.get_memory_statistics(files_implemented)
+
+        print("=" * 60)
+        print("🎯 CONCISE MEMORY AGENT STATE (Write-File-Based)")
+        print("=" * 60)
+        print(f"Last write_file detected: {stats['last_write_file_detected']}")
+        print(f"Should clear memory next: {stats['should_clear_memory_next']}")
+        print(f"Files implemented: {stats['implemented_files_tracked']}")
+        print(f"Current round: {stats['current_round']}")
+        print(f"Concise mode active: {stats['concise_mode_active']}")
+        print(f"Current round tool results: {stats['current_round_tool_results']}")
+        print(f"Essential tools recorded: {stats['essential_tools_recorded']}")
+        print(f"Implemented files tracked: {len(self.implemented_files)}")
+        print(f"Implemented files list: {self.implemented_files}")
+        print(f"Code summary file exists: {os.path.exists(self.code_summary_path)}")
+        print(f"Next Steps available: {stats['next_steps_available']}")
+        print(f"Next Steps length: {stats['next_steps_length']} chars")
+        if self.current_next_steps.strip():
+            print(f"Next Steps preview: {self.current_next_steps[:100]}...")
+        print("")
+        print("📋 FILE TRACKING:")
+        print(f"  Total files in plan: {stats['total_files_in_plan']}")
+        print(f"  Files implemented: {stats['files_implemented_count']}")
+        print(f"  Files remaining: {stats['files_remaining_count']}")
+        print(f"  Progress: {stats['implementation_progress_percent']:.1f}%")
+        if stats["unimplemented_files_list"]:
+            print(f"  Next possible files: {stats['unimplemented_files_list'][:3]}...")
+        print("")
+        print(
+            "📊 NEW LOGIC: write_file → clear memory → accumulate tools → next write_file"
+        )
+        print("📊 NEXT STEPS: Stored separately from file, included in tool results")
+        print(
+            "📊 FILE TRACKING: All files extracted from plan, unimplemented files guide LLM decisions"
+        )
+        print("📊 Essential Tools Tracked:")
+        essential_tools = [
+            "read_code_mem",
+            "read_file",
+            "write_file",
+            "execute_python",
+            "execute_bash",
+            "search_code",
+            "search_reference_code",
+            "get_file_structure",
+        ]
+        for tool in essential_tools:
+            tool_count = sum(
+                1 for r in self.current_round_tool_results if r["tool_name"] == tool
+            )
+            print(f"  - {tool}: {tool_count} calls")
+        print("=" * 60)

projects/ui/DeepCode/workflows/agents/memory_agent_concise_index.py

@@ -0,0 +1,1491 @@
+"""
+Concise Memory Agent for Code Implementation Workflow
+
+This memory agent implements a focused approach:
+1. Before first file: Normal conversation flow
+2. After first file: Keep only system_prompt + initial_plan + current round tool results
+3. Clean slate for each new code file generation
+
+Key Features:
+- Preserves system prompt and initial plan always
+- After first file generation, discards previous conversation history
+- Keeps only current round tool results from essential tools:
+  * read_code_mem, read_file, write_file
+  * execute_python, execute_bash
+  * search_code, search_reference_code, get_file_structure
+- Provides clean, focused input for next write_file operation
+"""
+
+import json
+import logging
+import os
+import time
+from datetime import datetime
+from typing import Dict, Any, List, Optional
+
+
+class ConciseMemoryAgent:
+    """
+    Concise Memory Agent - Focused Information Retention
+
+    Core Philosophy:
+    - Preserve essential context (system prompt + initial plan)
+    - After first file generation, use clean slate approach
+    - Keep only current round tool results from all essential MCP tools
+    - Remove conversational clutter and previous tool calls
+
+    Essential Tools Tracked:
+    - File Operations: read_code_mem, read_file, write_file
+    - Code Analysis: search_code, search_reference_code, get_file_structure
+    - Execution: execute_python, execute_bash
+    """
+
+    def __init__(
+        self,
+        initial_plan_content: str,
+        logger: Optional[logging.Logger] = None,
+        target_directory: Optional[str] = None,
+        default_models: Optional[Dict[str, str]] = None,
+    ):
+        """
+        Initialize Concise Memory Agent
+
+        Args:
+            initial_plan_content: Content of initial_plan.txt
+            logger: Logger instance
+            target_directory: Target directory for saving summaries
+            default_models: Default models configuration from workflow
+        """
+        self.logger = logger or self._create_default_logger()
+        self.initial_plan = initial_plan_content
+
+        # Store default models configuration
+        self.default_models = default_models or {
+            "anthropic": "claude-sonnet-4-20250514",
+            "openai": "gpt-4o",
+        }
+
+        # Memory state tracking - new logic: trigger after each write_file
+        self.last_write_file_detected = (
+            False  # Track if write_file was called in current iteration
+        )
+        self.should_clear_memory_next = False  # Flag to clear memory in next round
+        self.current_round = 0
+
+        # Parse phase structure from initial plan
+        self.phase_structure = self._parse_phase_structure()
+
+        # Extract all files from file structure in initial plan
+        self.all_files_list = self._extract_all_files_from_plan()
+
+        # Memory configuration
+        if target_directory:
+            self.save_path = target_directory
+        else:
+            self.save_path = "./deepcode_lab/papers/1/"
+
+        # Code summary file path
+        self.code_summary_path = os.path.join(
+            self.save_path, "implement_code_summary.md"
+        )
+
+        # Current round tool results storage
+        self.current_round_tool_results = []
+
+        # Track all implemented files
+        self.implemented_files = []
+
+        # Store Next Steps information temporarily (not saved to file)
+        self.current_next_steps = ""
+
+        self.logger.info(
+            f"Concise Memory Agent initialized with target directory: {self.save_path}"
+        )
+        self.logger.info(f"Code summary will be saved to: {self.code_summary_path}")
+        # self.logger.info(f"🤖 Using models - Anthropic: {self.default_models['anthropic']}, OpenAI: {self.default_models['openai']}")
+        self.logger.info(
+            "📝 NEW LOGIC: Memory clearing triggered after each write_file call"
+        )
+
+    def _create_default_logger(self) -> logging.Logger:
+        """Create default logger"""
+        logger = logging.getLogger(f"{__name__}.ConciseMemoryAgent")
+        logger.setLevel(logging.INFO)
+        return logger
+
+    def _parse_phase_structure(self) -> Dict[str, List[str]]:
+        """Parse implementation phases from initial plan"""
+        try:
+            phases = {}
+            lines = self.initial_plan.split("\n")
+            current_phase = None
+
+            for line in lines:
+                if "Phase" in line and ":" in line:
+                    # Extract phase name
+                    phase_parts = line.split(":")
+                    if len(phase_parts) >= 2:
+                        current_phase = phase_parts[0].strip()
+                        phases[current_phase] = []
+                elif current_phase and line.strip().startswith("-"):
+                    # This is a file in the current phase
+                    file_line = line.strip()[1:].strip()
+                    if file_line.startswith("`") and file_line.endswith("`"):
+                        file_name = file_line[1:-1]
+                        phases[current_phase].append(file_name)
+                elif current_phase and not line.strip():
+                    # Empty line might indicate end of phase
+                    continue
+                elif current_phase and line.strip().startswith("###"):
+                    # New section, end current phase
+                    current_phase = None
+
+            return phases
+
+        except Exception as e:
+            self.logger.warning(f"Failed to parse phase structure: {e}")
+            return {}
+
+    def _extract_all_files_from_plan(self) -> List[str]:
+        """
+        Extract all file paths from the file_structure section in initial plan
+        Handles multiple formats: tree structure, YAML, and simple lists
+
+        Returns:
+            List of all file paths that should be implemented
+        """
+        try:
+            lines = self.initial_plan.split("\n")
+            files = []
+
+            # Method 1: Try to extract from tree structure in file_structure section
+            files.extend(self._extract_from_tree_structure(lines))
+
+            # Method 2: If no files found, try to extract from simple list format
+            if not files:
+                files.extend(self._extract_from_simple_list(lines))
+
+            # Method 3: If still no files, try to extract from anywhere in the plan
+            if not files:
+                files.extend(self._extract_from_plan_content(lines))
+
+            # Clean and validate file paths
+            cleaned_files = self._clean_and_validate_files(files)
+
+            # Log the extracted files
+            self.logger.info(
+                f"📁 Extracted {len(cleaned_files)} files from initial plan"
+            )
+            if cleaned_files:
+                self.logger.info(f"📁 Sample files: {cleaned_files[:3]}...")
+
+            return cleaned_files
+
+        except Exception as e:
+            self.logger.error(f"Failed to extract files from initial plan: {e}")
+            return []
+
+    def _extract_from_tree_structure(self, lines: List[str]) -> List[str]:
+        """Extract files from tree structure format - only from file_structure section"""
+        files = []
+        in_file_structure = False
+        path_stack = []
+
+        for line in lines:
+            # Check if we're in the file_structure section
+            if "file_structure:" in line or "file_structure |" in line:
+                in_file_structure = True
+                continue
+            # Check for end of file_structure section (next YAML key)
+            elif (
+                in_file_structure
+                and line.strip()
+                and not line.startswith(" ")
+                and ":" in line
+            ):
+                # This looks like a new YAML section, stop parsing
+                break
+            elif not in_file_structure:
+                continue
+
+            if not line.strip():
+                continue
+
+            # Skip lines that look like YAML keys (contain ":" but not file paths)
+            if ":" in line and not ("." in line and "/" in line):
+                continue
+
+            stripped_line = line.strip()
+
+            # Detect root directory (directory name ending with / at minimal indentation)
+            if (
+                stripped_line.endswith("/")
+                and len(line) - len(line.lstrip())
+                <= 4  # Minimal indentation (0-4 spaces)
+                and not any(char in line for char in ["├", "└", "│", "─"])
+            ):  # No tree characters
+                root_directory = stripped_line.rstrip("/")
+                path_stack = [root_directory]
+                continue
+
+            # Only process lines that have tree structure
+            if not any(char in line for char in ["├", "└", "│", "─"]):
+                continue
+
+            # Parse tree structure depth by analyzing the line structure
+            # Count │ characters before the actual item, or use indentation as fallback
+            pipe_count = 0
+
+            for i, char in enumerate(line):
+                if char == "│":
+                    pipe_count += 1
+                elif char in ["├", "└"]:
+                    break
+
+            # Calculate depth: use pipe count if available, otherwise use indentation
+            if pipe_count > 0:
+                depth = pipe_count + 1  # +1 because the actual item is one level deeper
+            else:
+                # Use indentation to determine depth (every 4 spaces = 1 level)
+                indent_spaces = len(line) - len(line.lstrip())
+                depth = max(1, indent_spaces // 4)  # At least depth 1
+
+            # Clean the line to get the item name
+            clean_line = line
+            for char in ["├──", "└──", "├", "└", "│", "─"]:
+                clean_line = clean_line.replace(char, "")
+            clean_line = clean_line.strip()
+
+            if not clean_line or ":" in clean_line:
+                continue
+
+            # Extract filename (remove comments)
+            if "#" in clean_line:
+                filename = clean_line.split("#")[0].strip()
+            else:
+                filename = clean_line.strip()
+
+            # Skip empty filenames
+            if not filename:
+                continue
+
+            # Adjust path stack to current depth
+            while len(path_stack) < depth:
+                path_stack.append("")
+            path_stack = path_stack[:depth]
+
+            # Determine if it's a directory or file
+            is_directory = (
+                filename.endswith("/")
+                or (
+                    "." not in filename
+                    and filename not in ["README", "requirements.txt", "setup.py"]
+                )
+                or filename
+                in [
+                    "core",
+                    "networks",
+                    "environments",
+                    "baselines",
+                    "evaluation",
+                    "experiments",
+                    "utils",
+                    "src",
+                    "lib",
+                    "app",
+                ]
+            )
+
+            if is_directory:
+                directory_name = filename.rstrip("/")
+                if directory_name and ":" not in directory_name:
+                    path_stack.append(directory_name)
+            else:
+                # It's a file, construct full path
+                if path_stack:
+                    full_path = "/".join(path_stack) + "/" + filename
+                else:
+                    full_path = filename
+                files.append(full_path)
+
+        return files
+
+    def _extract_from_simple_list(self, lines: List[str]) -> List[str]:
+        """Extract files from simple list format (- filename)"""
+        files = []
+
+        for line in lines:
+            line = line.strip()
+            if line.startswith("- ") and not line.startswith('- "'):
+                # Remove leading "- " and clean up
+                filename = line[2:].strip()
+
+                # Remove quotes if present
+                if filename.startswith('"') and filename.endswith('"'):
+                    filename = filename[1:-1]
+
+                # Check if it looks like a file (has extension)
+                if "." in filename and "/" in filename:
+                    files.append(filename)
+
+        return files
+
+    def _extract_from_plan_content(self, lines: List[str]) -> List[str]:
+        """Extract files from anywhere in the plan content"""
+        files = []
+
+        # Look for common file patterns
+        import re
+
+        file_patterns = [
+            r"([a-zA-Z0-9_\-/]+\.[a-zA-Z0-9]+)",  # filename.ext
+            r'"([a-zA-Z0-9_\-/]+\.[a-zA-Z0-9]+)"',  # "filename.ext"
+        ]
+
+        for line in lines:
+            for pattern in file_patterns:
+                matches = re.findall(pattern, line)
+                for match in matches:
+                    # Only include if it looks like a code file (exclude media files)
+                    if "/" in match and any(
+                        ext in match
+                        for ext in [
+                            ".py",
+                            ".js",
+                            ".html",
+                            ".css",
+                            ".md",
+                            ".txt",
+                            ".json",
+                            ".yaml",
+                            ".yml",
+                            ".xml",
+                            ".sql",
+                            ".sh",
+                            ".ts",
+                            ".jsx",
+                            ".tsx",
+                        ]
+                    ):
+                        files.append(match)
+
+        return files
+
+    def _clean_and_validate_files(self, files: List[str]) -> List[str]:
+        """Clean and validate extracted file paths - only keep code files"""
+        cleaned_files = []
+
+        # Define code file extensions we want to track
+        code_extensions = [
+            ".py",
+            ".js",
+            ".html",
+            ".css",
+            ".md",
+            ".txt",
+            ".json",
+            ".yaml",
+            ".yml",
+            ".xml",
+            ".sql",
+            ".sh",
+            ".bat",
+            ".dockerfile",
+            ".env",
+            ".gitignore",
+            ".ts",
+            ".jsx",
+            ".tsx",
+            ".vue",
+            ".php",
+            ".rb",
+            ".go",
+            ".rs",
+            ".cpp",
+            ".c",
+            ".h",
+            ".hpp",
+            ".java",
+            ".kt",
+            ".swift",
+            ".dart",
+        ]
+
+        for file_path in files:
+            # Clean the path
+            cleaned_path = file_path.strip().strip('"').strip("'")
+
+            # Skip if empty
+            if not cleaned_path:
+                continue
+
+            # Skip directories (no file extension)
+            if "." not in cleaned_path.split("/")[-1]:
+                continue
+
+            # Only include files with code extensions
+            has_code_extension = any(
+                cleaned_path.lower().endswith(ext) for ext in code_extensions
+            )
+            if not has_code_extension:
+                continue
+
+            # Skip files that look like YAML keys or config entries
+            if (
+                ":" in cleaned_path
+                and not cleaned_path.endswith(".yaml")
+                and not cleaned_path.endswith(".yml")
+            ):
+                continue
+
+            # Skip paths that contain invalid characters for file paths
+            if any(invalid_char in cleaned_path for invalid_char in ['"', "'", "|"]):
+                continue
+
+            # Add to cleaned list if not already present
+            if cleaned_path not in cleaned_files:
+                cleaned_files.append(cleaned_path)
+
+        return sorted(cleaned_files)
+
+    def record_file_implementation(
+        self, file_path: str, implementation_content: str = ""
+    ):
+        """
+        Record a newly implemented file (simplified version)
+        NEW LOGIC: File implementation is tracked via write_file tool detection
+
+        Args:
+            file_path: Path of the implemented file
+            implementation_content: Content of the implemented file
+        """
+        # Add file to implemented files list if not already present
+        if file_path not in self.implemented_files:
+            self.implemented_files.append(file_path)
+
+        self.logger.info(f"📝 File implementation recorded: {file_path}")
+
+    async def create_code_implementation_summary(
+        self,
+        client,
+        client_type: str,
+        file_path: str,
+        implementation_content: str,
+        files_implemented: int,
+    ) -> str:
+        """
+        Create LLM-based code implementation summary after writing a file
+        Uses LLM to analyze and summarize the implemented code
+
+        Args:
+            client: LLM client instance
+            client_type: Type of LLM client ("anthropic" or "openai")
+            file_path: Path of the implemented file
+            implementation_content: Content of the implemented file
+            files_implemented: Number of files implemented so far
+
+        Returns:
+            LLM-generated formatted code implementation summary
+        """
+        try:
+            # Record the file implementation first
+            self.record_file_implementation(file_path, implementation_content)
+
+            # Create prompt for LLM summary
+            summary_prompt = self._create_code_summary_prompt(
+                file_path, implementation_content, files_implemented
+            )
+            summary_messages = [{"role": "user", "content": summary_prompt}]
+
+            # Get LLM-generated summary
+            llm_response = await self._call_llm_for_summary(
+                client, client_type, summary_messages
+            )
+            llm_summary = llm_response.get("content", "")
+
+            # Extract different sections from LLM summary
+            sections = self._extract_summary_sections(llm_summary)
+
+            # Store Next Steps in temporary variable (not saved to file)
+            self.current_next_steps = sections.get("next_steps", "")
+            if self.current_next_steps:
+                self.logger.info("📝 Next Steps stored temporarily (not saved to file)")
+
+            # Format summary with only Implementation Progress and Dependencies for file saving
+            file_summary_content = ""
+            if sections.get("core_purpose"):
+                file_summary_content += sections["core_purpose"] + "\n\n"
+            if sections.get("public_interface"):
+                file_summary_content += sections["public_interface"] + "\n\n"
+            if sections.get("internal_dependencies"):
+                file_summary_content += sections["internal_dependencies"] + "\n\n"
+            if sections.get("external_dependencies"):
+                file_summary_content += sections["external_dependencies"] + "\n\n"
+            if sections.get("implementation_notes"):
+                file_summary_content += sections["implementation_notes"] + "\n\n"
+
+            # Create the formatted summary for file saving (without Next Steps)
+            formatted_summary = self._format_code_implementation_summary(
+                file_path, file_summary_content.strip(), files_implemented
+            )
+
+            # Save to implement_code_summary.md (append mode) - only Implementation Progress and Dependencies
+            await self._save_code_summary_to_file(formatted_summary, file_path)
+
+            self.logger.info(f"Created and saved code summary for: {file_path}")
+            return formatted_summary
+
+        except Exception as e:
+            self.logger.error(
+                f"Failed to create LLM-based code implementation summary: {e}"
+            )
+            # Fallback to simple summary
+            return self._create_fallback_code_summary(
+                file_path, implementation_content, files_implemented
+            )
+
+    def _create_code_summary_prompt(
+        self, file_path: str, implementation_content: str, files_implemented: int
+    ) -> str:
+        """
+        Create prompt for LLM to generate code implementation summary
+
+        Args:
+            file_path: Path of the implemented file
+            implementation_content: Content of the implemented file
+            files_implemented: Number of files implemented so far
+
+        Returns:
+            Prompt for LLM summarization
+        """
+        current_round = self.current_round
+
+        # Get formatted file lists
+        file_lists = self.get_formatted_files_lists()
+        implemented_files_list = file_lists["implemented"]
+        unimplemented_files_list = file_lists["unimplemented"]
+
+        prompt = f"""You are an expert code implementation summarizer. Analyze the implemented code file and create a structured summary.
+
+**🚨 CRITICAL: The files listed below are ALREADY IMPLEMENTED - DO NOT suggest them in Next Steps! 🚨**
+
+**All Previously Implemented Files:**
+{implemented_files_list}
+
+**Remaining Unimplemented Files (choose ONLY from these for Next Steps):**
+{unimplemented_files_list}
+
+**Current Implementation Context:**
+- **File Implemented**: {file_path}
+- **Current Round**: {current_round}
+- **Total Files Implemented**: {files_implemented}
+
+
+**Initial Plan Reference:**
+{self.initial_plan[:]}
+
+**Implemented Code Content:**
+```
+{implementation_content[:]}
+```
+
+**Required Summary Format:**
+
+**Core Purpose** (provide a general overview of the file's main responsibility):
+- {{1-2 sentence description of file's main responsibility}}
+
+**Public Interface** (what other files can use, if any):
+- Class {{ClassName}}: {{purpose}} | Key methods: {{method_names}} | Constructor params: {{params}}
+- Function {{function_name}}({{params}}): {{purpose}} -> {{return_type}}: {{purpose}}
+- Constants/Types: {{name}}: {{value/description}}
+
+**Internal Dependencies** (what this file imports/requires, if any):
+- From {{module/file}}: {{specific_imports}}
+- External packages: {{package_name}} - {{usage_context}}
+
+**External Dependencies** (what depends on this file, if any):
+- Expected to be imported by: {{likely_consumer_files}}
+- Key exports used elsewhere: {{main_interfaces}}
+
+**Implementation Notes**: (if any)
+- Architecture decisions: {{key_choices_made}}
+- Cross-File Relationships: {{how_files_work_together}}
+
+**Next Steps**: List the code file (ONLY ONE) that will be implemented in the next round (MUST choose from "Remaining Unimplemented Files" above)
+  Format: Code will be implemented: {{file_path}}
+  **NEVER suggest any file from the "All Previously Implemented Files" list!**
+
+**Instructions:**
+- Be precise and concise
+- Focus on function interfaces that other files will need
+- Extract actual function signatures from the code
+- **CRITICAL: For Next Steps, ONLY choose ONE file from the "Remaining Unimplemented Files" list above**
+- **NEVER suggest implementing a file that is already in the implemented files list**
+- Choose the next file based on logical dependencies and implementation order
+- Use the exact format specified above
+
+**Summary:**"""
+
+        return prompt
+
+    # TODO: The prompt is not good, need to be improved
+    # **Implementation Progress**: List the code file completed in current round and core implementation ideas
+    #   Format: {{file_path}}: {{core implementation ideas}}
+
+    # **Dependencies**: According to the File Structure and initial plan, list functions that may be called by other files
+    #   Format: {{file_path}}: Function {{function_name}}: core ideas--{{ideas}}; Required parameters--{{params}}; Return parameters--{{returns}}
+    #   Required packages: {{packages}}
+
+    def _extract_summary_sections(self, llm_summary: str) -> Dict[str, str]:
+        """
+        Extract different sections from LLM-generated summary
+
+        Args:
+            llm_summary: Raw LLM-generated summary text
+
+        Returns:
+            Dictionary with extracted sections: core_purpose, public_interface, internal_dependencies,
+            external_dependencies, implementation_notes, next_steps
+        """
+        sections = {
+            "core_purpose": "",
+            "public_interface": "",
+            "internal_dependencies": "",
+            "external_dependencies": "",
+            "implementation_notes": "",
+            "next_steps": "",
+        }
+
+        try:
+            lines = llm_summary.split("\n")
+            current_section = None
+            current_content = []
+
+            for line in lines:
+                line_lower = line.lower().strip()
+
+                # Check for section headers
+                if "core purpose" in line_lower:
+                    if current_section and current_content:
+                        sections[current_section] = "\n".join(current_content).strip()
+                    current_section = "core_purpose"
+                    current_content = [line]  # Include the header
+                elif "public interface" in line_lower:
+                    if current_section and current_content:
+                        sections[current_section] = "\n".join(current_content).strip()
+                    current_section = "public_interface"
+                    current_content = [line]  # Include the header
+                elif "internal dependencies" in line_lower:
+                    if current_section and current_content:
+                        sections[current_section] = "\n".join(current_content).strip()
+                    current_section = "internal_dependencies"
+                    current_content = [line]  # Include the header
+                elif "external dependencies" in line_lower:
+                    if current_section and current_content:
+                        sections[current_section] = "\n".join(current_content).strip()
+                    current_section = "external_dependencies"
+                    current_content = [line]  # Include the header
+                elif "implementation notes" in line_lower:
+                    if current_section and current_content:
+                        sections[current_section] = "\n".join(current_content).strip()
+                    current_section = "implementation_notes"
+                    current_content = [line]  # Include the header
+                elif "next steps" in line_lower:
+                    if current_section and current_content:
+                        sections[current_section] = "\n".join(current_content).strip()
+                    current_section = "next_steps"
+                    current_content = [line]  # Include the header
+                else:
+                    # Add content to current section
+                    if current_section:
+                        current_content.append(line)
+
+            # Don't forget the last section
+            if current_section and current_content:
+                sections[current_section] = "\n".join(current_content).strip()
+
+            self.logger.info(f"📋 Extracted sections: {list(sections.keys())}")
+
+        except Exception as e:
+            self.logger.error(f"Failed to extract summary sections: {e}")
+            # Fallback: put everything in core_purpose
+            sections["core_purpose"] = llm_summary
+
+        return sections
+
+    def _format_code_implementation_summary(
+        self, file_path: str, llm_summary: str, files_implemented: int
+    ) -> str:
+        """
+        Format the LLM-generated summary into the final structure
+
+        Args:
+            file_path: Path of the implemented file
+            llm_summary: LLM-generated summary content
+            files_implemented: Number of files implemented so far
+
+        Returns:
+            Formatted summary
+        """
+        timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+
+        # # Create formatted list of implemented files
+        # implemented_files_list = (
+        #     "\n".join([f"- {file}" for file in self.implemented_files])
+        #     if self.implemented_files
+        #     else "- None yet"
+        # )
+
+        #         formatted_summary = f"""# Code Implementation Summary
+        # **All Previously Implemented Files:**
+        # {implemented_files_list}
+        # **Generated**: {timestamp}
+        # **File Implemented**: {file_path}
+        # **Total Files Implemented**: {files_implemented}
+
+        # {llm_summary}
+
+        # ---
+        # *Auto-generated by Memory Agent*
+        # """
+        formatted_summary = f"""# Code Implementation Summary
+**Generated**: {timestamp}
+**File Implemented**: {file_path}
+
+{llm_summary}
+
+---
+*Auto-generated by Memory Agent*
+"""
+        return formatted_summary
+
+    def _create_fallback_code_summary(
+        self, file_path: str, implementation_content: str, files_implemented: int
+    ) -> str:
+        """
+        Create fallback summary when LLM is unavailable
+
+        Args:
+            file_path: Path of the implemented file
+            implementation_content: Content of the implemented file
+            files_implemented: Number of files implemented so far
+
+        Returns:
+            Fallback summary
+        """
+        # Create formatted list of implemented files
+        implemented_files_list = (
+            "\n".join([f"- {file}" for file in self.implemented_files])
+            if self.implemented_files
+            else "- None yet"
+        )
+
+        summary = f"""# Code Implementation Summary
+**All Previously Implemented Files:**
+{implemented_files_list}
+**Generated**: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}
+**File Implemented**: {file_path}
+**Total Files Implemented**: {files_implemented}
+**Summary failed to generate.**
+
+---
+*Auto-generated by Concise Memory Agent (Fallback Mode)*
+"""
+        return summary
+
+    async def _save_code_summary_to_file(self, new_summary: str, file_path: str):
+        """
+        Append code implementation summary to implement_code_summary.md
+        Accumulates all implementations with clear separators
+
+        Args:
+            new_summary: New summary content to append
+            file_path: Path of the file for which the summary was generated
+        """
+        try:
+            # Create directory if it doesn't exist
+            os.makedirs(os.path.dirname(self.code_summary_path), exist_ok=True)
+
+            # Check if file exists to determine if we need header
+            file_exists = os.path.exists(self.code_summary_path)
+
+            # Open in append mode to accumulate all implementations
+            with open(self.code_summary_path, "a", encoding="utf-8") as f:
+                if not file_exists:
+                    # Write header for new file
+                    f.write("# Code Implementation Progress Summary\n")
+                    f.write("*Accumulated implementation progress for all files*\n\n")
+
+                # Add clear separator between implementations
+                f.write("\n" + "=" * 80 + "\n")
+                f.write(
+                    f"## IMPLEMENTATION File {file_path}; ROUND {self.current_round} \n"
+                )
+                f.write("=" * 80 + "\n\n")
+
+                # Write the new summary
+                f.write(new_summary)
+                f.write("\n\n")
+
+            self.logger.info(
+                f"Appended LLM-based code implementation summary to: {self.code_summary_path}"
+            )
+
+        except Exception as e:
+            self.logger.error(f"Failed to save code implementation summary: {e}")
+
+    async def _call_llm_for_summary(
+        self, client, client_type: str, summary_messages: List[Dict]
+    ) -> Dict[str, Any]:
+        """
+        Call LLM for code implementation summary generation ONLY
+
+        This method is used only for creating code implementation summaries,
+        NOT for conversation summarization which has been removed.
+        """
+        if client_type == "anthropic":
+            response = await client.messages.create(
+                model=self.default_models["anthropic"],
+                system="You are an expert code implementation summarizer. Create structured summaries of implemented code files that preserve essential information about functions, dependencies, and implementation approaches.",
+                messages=summary_messages,
+                max_tokens=5000,
+                temperature=0.2,
+            )
+
+            content = ""
+            for block in response.content:
+                if block.type == "text":
+                    content += block.text
+
+            return {"content": content}
+
+        elif client_type == "openai":
+            openai_messages = [
+                {
+                    "role": "system",
+                    "content": "You are an expert code implementation summarizer. Create structured summaries of implemented code files that preserve essential information about functions, dependencies, and implementation approaches.",
+                }
+            ]
+            openai_messages.extend(summary_messages)
+
+            # Try max_tokens and temperature first, fallback to max_completion_tokens without temperature if unsupported
+            try:
+                response = await client.chat.completions.create(
+                    model=self.default_models["openai"],
+                    messages=openai_messages,
+                    max_tokens=5000,
+                    temperature=0.2,
+                )
+            except Exception as e:
+                if "max_tokens" in str(e) and "max_completion_tokens" in str(e):
+                    # Retry with max_completion_tokens and no temperature for models that require it
+                    response = await client.chat.completions.create(
+                        model=self.default_models["openai"],
+                        messages=openai_messages,
+                        max_completion_tokens=5000,
+                    )
+                else:
+                    raise
+
+            return {"content": response.choices[0].message.content or ""}
+
+        else:
+            raise ValueError(f"Unsupported client type: {client_type}")
+
+    def start_new_round(self, iteration: Optional[int] = None):
+        """Start a new dialogue round and reset tool results
+
+        Args:
+            iteration: Optional iteration number from workflow to sync with current_round
+        """
+        if iteration is not None:
+            # Sync with workflow iteration
+            self.current_round = iteration
+            # self.logger.info(f"🔄 Synced round with workflow iteration {iteration}")
+        else:
+            # Default behavior: increment round counter
+            self.current_round += 1
+            self.logger.info(f"🔄 Started new round {self.current_round}")
+
+        self.current_round_tool_results = []  # Clear previous round results
+        # Note: Don't reset last_write_file_detected and should_clear_memory_next here
+        # These flags persist across rounds until memory optimization is applied
+        # self.logger.info(f"🔄 Round {self.current_round} - Tool results cleared, memory flags preserved")
+
+    def record_tool_result(
+        self, tool_name: str, tool_input: Dict[str, Any], tool_result: Any
+    ):
+        """
+        Record tool result for current round and detect write_file calls
+
+        Args:
+            tool_name: Name of the tool called
+            tool_input: Input parameters for the tool
+            tool_result: Result returned by the tool
+        """
+        # Detect write_file calls to trigger memory clearing
+        if tool_name == "write_file":
+            self.last_write_file_detected = True
+            self.should_clear_memory_next = True
+
+            # self.logger.info(f"🔄 WRITE_FILE DETECTED: {file_path} - Memory will be cleared in next round")
+
+        # Only record specific tools that provide essential information
+        essential_tools = [
+            "read_code_mem",  # Read code summary from implement_code_summary.md
+            "read_file",  # Read file contents
+            "write_file",  # Write file contents (important for tracking implementations)
+            "execute_python",  # Execute Python code (for testing/validation)
+            "execute_bash",  # Execute bash commands (for build/execution)
+            "search_code",  # Search code patterns
+            "search_reference_code",  # Search reference code (if available)
+            "get_file_structure",  # Get file structure (for understanding project layout)
+        ]
+
+        if tool_name in essential_tools:
+            tool_record = {
+                "tool_name": tool_name,
+                "tool_input": tool_input,
+                "tool_result": tool_result,
+                "timestamp": time.time(),
+            }
+            self.current_round_tool_results.append(tool_record)
+            # self.logger.info(f"📊 Essential tool result recorded: {tool_name} ({len(self.current_round_tool_results)} total)")
+
+    def should_use_concise_mode(self) -> bool:
+        """
+        Check if concise memory mode should be used
+
+        Returns:
+            True if first file has been generated and concise mode should be active
+        """
+        return self.last_write_file_detected
+
+    def create_concise_messages(
+        self,
+        system_prompt: str,
+        messages: List[Dict[str, Any]],
+        files_implemented: int,
+    ) -> List[Dict[str, Any]]:
+        """
+        Create concise message list for LLM input
+        NEW LOGIC: Always clear after write_file, keep system_prompt + initial_plan + current round tools
+
+        Args:
+            system_prompt: Current system prompt
+            messages: Original message list
+            files_implemented: Number of files implemented so far
+
+        Returns:
+            Concise message list containing only essential information
+        """
+        if not self.last_write_file_detected:
+            # Before any write_file, use normal flow
+            self.logger.info(
+                "🔄 Using normal conversation flow (before any write_file)"
+            )
+            return messages
+
+        # After write_file detection, use concise approach with clean slate
+        self.logger.info(
+            f"🎯 Using CONCISE memory mode - Clear slate after write_file, Round {self.current_round}"
+        )
+
+        concise_messages = []
+
+        # Get formatted file lists
+        file_lists = self.get_formatted_files_lists()
+        implemented_files_list = file_lists["implemented"]
+
+        # 1. Add initial plan message (always preserved)
+        initial_plan_message = {
+            "role": "user",
+            "content": f"""**Task: Implement code based on the following reproduction plan**
+
+**Code Reproduction Plan:**
+{self.initial_plan}
+
+**Working Directory:** Current workspace
+
+**All Previously Implemented Files:**
+{implemented_files_list}
+
+**Current Status:** {files_implemented} files implemented
+
+**Objective:** Continue implementation by analyzing dependencies and implementing the next required file according to the plan's priority order.""",
+        }
+
+        # Append Next Steps information if available
+        if self.current_next_steps.strip():
+            initial_plan_message["content"] += (
+                f"\n\n**Next Steps (from previous analysis):**\n{self.current_next_steps}"
+            )
+
+        # Debug output for unimplemented files (clean format without dashes)
+        unimplemented_files = self.get_unimplemented_files()
+        print("✅ Unimplemented Files:")
+        for file_path in unimplemented_files:
+            print(f"{file_path}")
+        if self.current_next_steps.strip():
+            print(f"\n📋 {self.current_next_steps}")
+
+        concise_messages.append(initial_plan_message)
+
+        # 2. Add Knowledge Base
+        knowledge_base_message = {
+            "role": "user",
+            "content": f"""**Below is the Knowledge Base of the LATEST implemented code file:**
+{self._read_code_knowledge_base()}
+
+**Development Cycle - START HERE:**
+
+**For NEW file implementation:**
+1. **You need to call read_code_mem(already_implemented_file_path)** to understand existing implementations and dependencies - agent should choose relevant ALREADY IMPLEMENTED file paths for reference, NOT the new file you want to create
+2. `search_code_references` → OPTIONALLY search reference patterns for inspiration (use for reference only, original paper specs take priority)
+3. `write_file` → Create the complete code implementation based on original paper requirements
+4. `execute_python` or `execute_bash` → Test the partial implementation if needed
+
+**When all files implemented:**
+**Use execute_python or execute_bash** to test the complete implementation""",
+        }
+        concise_messages.append(knowledge_base_message)
+
+        # 3. Add current tool results (essential information for next file generation)
+        if self.current_round_tool_results:
+            tool_results_content = self._format_tool_results()
+
+            # # Append Next Steps information if available
+            # if self.current_next_steps.strip():
+            #     tool_results_content += f"\n\n**Next Steps (from previous analysis):**\n{self.current_next_steps}"
+
+            tool_results_message = {
+                "role": "user",
+                "content": f"""**Current Tool Results:**
+{tool_results_content}""",
+            }
+            concise_messages.append(tool_results_message)
+        else:
+            # If no tool results yet, add guidance for next steps
+            guidance_content = f"""**Current Round:** {self.current_round}
+
+**Development Cycle - START HERE:**
+
+**For NEW file implementation:**
+1. **You need to call read_code_mem(already_implemented_file_path)** to understand existing implementations and dependencies - agent should choose relevant ALREADY IMPLEMENTED file paths for reference, NOT the new file you want to create
+2. `search_code_references` → OPTIONALLY search reference patterns for inspiration (use for reference only, original paper specs take priority)
+3. Write_file can be used to implement the new component
+4. Finally: Use execute_python or execute_bash for testing (if needed)
+
+**When all files implemented:**
+1. **Use execute_python or execute_bash** to test the complete implementation"""
+
+            # # Append Next Steps information if available (even when no tool results)
+            # if self.current_next_steps.strip():
+            #     guidance_content += f"\n\n**Next Steps (from previous analysis):**\n{self.current_next_steps}"
+
+            guidance_message = {
+                "role": "user",
+                "content": guidance_content,
+            }
+            concise_messages.append(guidance_message)
+        # **Available Essential Tools:** read_code_mem, write_file, execute_python, execute_bash
+        # **Remember:** Start with read_code_mem when implementing NEW files to understand existing code. When all files are implemented, focus on testing and completion. Implement according to the original paper's specifications - any reference code is for inspiration only."""
+        # self.logger.info(f"✅ Concise messages created: {len(concise_messages)} messages (original: {len(messages)})")
+        return concise_messages
+
+    def _read_code_knowledge_base(self) -> Optional[str]:
+        """
+        Read the implement_code_summary.md file as code knowledge base
+        Returns only the final/latest implementation entry, not all historical entries
+
+        Returns:
+            Content of the latest implementation entry if it exists, None otherwise
+        """
+        try:
+            if os.path.exists(self.code_summary_path):
+                with open(self.code_summary_path, "r", encoding="utf-8") as f:
+                    content = f.read().strip()
+
+                if content:
+                    # Extract only the final/latest implementation entry
+                    return self._extract_latest_implementation_entry(content)
+                else:
+                    return None
+            else:
+                return None
+
+        except Exception as e:
+            self.logger.error(f"Failed to read code knowledge base: {e}")
+            return None
+
+    def _extract_latest_implementation_entry(self, content: str) -> Optional[str]:
+        """
+        Extract the latest/final implementation entry from the implement_code_summary.md content
+        Uses a simpler approach to find the last implementation section
+
+        Args:
+            content: Full content of implement_code_summary.md
+
+        Returns:
+            Latest implementation entry content, or None if not found
+        """
+        try:
+            import re
+
+            # Pattern to match the start of implementation sections
+            section_pattern = (
+                r"={80}\s*\n## IMPLEMENTATION File .+?; ROUND \d+\s*\n={80}"
+            )
+
+            # Find all implementation section starts
+            matches = list(re.finditer(section_pattern, content))
+
+            if not matches:
+                # No implementation sections found
+                lines = content.split("\n")
+                fallback_content = (
+                    "\n".join(lines[:10]) + "\n... (truncated for brevity)"
+                    if len(lines) > 10
+                    else content
+                )
+                self.logger.info(
+                    "📖 No implementation sections found, using fallback content"
+                )
+                return fallback_content
+
+            # Get the start position of the last implementation section
+            last_match = matches[-1]
+            start_pos = last_match.start()
+
+            # Take everything from the last section start to the end of content
+            latest_entry = content[start_pos:].strip()
+
+            # self.logger.info(f"📖 Extracted latest implementation entry from knowledge base")
+            # print(f"DEBUG: Extracted content length: {len(latest_entry)}")
+            # print(f"DEBUG: First 200 chars: {latest_entry[:]}")
+
+            return latest_entry
+
+        except Exception as e:
+            self.logger.error(f"Failed to extract latest implementation entry: {e}")
+            # Return last 1000 characters as fallback
+            return content[-500:] if len(content) > 500 else content
+
+    def _format_tool_results(self) -> str:
+        """
+        Format current round tool results for LLM input
+
+        Returns:
+            Formatted string of tool results
+        """
+        if not self.current_round_tool_results:
+            return "No tool results in current round."
+
+        formatted_results = []
+
+        for result in self.current_round_tool_results:
+            tool_name = result["tool_name"]
+            tool_input = result["tool_input"]
+            tool_result = result["tool_result"]
+
+            # Format based on tool type
+            if tool_name == "read_code_mem":
+                file_path = tool_input.get("file_path", "unknown")
+                formatted_results.append(f"""
+**read_code_mem Result for {file_path}:**
+{self._format_tool_result_content(tool_result)}
+""")
+            elif tool_name == "read_file":
+                file_path = tool_input.get("file_path", "unknown")
+                formatted_results.append(f"""
+**read_file Result for {file_path}:**
+{self._format_tool_result_content(tool_result)}
+""")
+            elif tool_name == "write_file":
+                file_path = tool_input.get("file_path", "unknown")
+                formatted_results.append(f"""
+**write_file Result for {file_path}:**
+{self._format_tool_result_content(tool_result)}
+""")
+            elif tool_name == "execute_python":
+                code_snippet = (
+                    tool_input.get("code", "")[:50] + "..."
+                    if len(tool_input.get("code", "")) > 50
+                    else tool_input.get("code", "")
+                )
+                formatted_results.append(f"""
+**execute_python Result (code: {code_snippet}):**
+{self._format_tool_result_content(tool_result)}
+""")
+            elif tool_name == "execute_bash":
+                command = tool_input.get("command", "unknown")
+                formatted_results.append(f"""
+**execute_bash Result (command: {command}):**
+{self._format_tool_result_content(tool_result)}
+""")
+            elif tool_name == "search_code":
+                pattern = tool_input.get("pattern", "unknown")
+                file_pattern = tool_input.get("file_pattern", "")
+                formatted_results.append(f"""
+**search_code Result (pattern: {pattern}, files: {file_pattern}):**
+{self._format_tool_result_content(tool_result)}
+""")
+            elif tool_name == "search_reference_code":
+                target_file = tool_input.get("target_file", "unknown")
+                keywords = tool_input.get("keywords", "")
+                formatted_results.append(f"""
+**search_reference_code Result for {target_file} (keywords: {keywords}):**
+{self._format_tool_result_content(tool_result)}
+""")
+            elif tool_name == "get_file_structure":
+                directory = tool_input.get(
+                    "directory_path", tool_input.get("path", "current")
+                )
+                formatted_results.append(f"""
+**get_file_structure Result for {directory}:**
+{self._format_tool_result_content(tool_result)}
+""")
+
+        return "\n".join(formatted_results)
+
+    def _format_tool_result_content(self, tool_result: Any) -> str:
+        """
+        Format tool result content for display
+
+        Args:
+            tool_result: Tool result to format
+
+        Returns:
+            Formatted string representation
+        """
+        if isinstance(tool_result, str):
+            # Try to parse as JSON for better formatting
+            try:
+                result_data = json.loads(tool_result)
+                if isinstance(result_data, dict):
+                    # Format key information
+                    if result_data.get("status") == "summary_found":
+                        return (
+                            f"Summary found:\n{result_data.get('summary_content', '')}"
+                        )
+                    elif result_data.get("status") == "no_summary":
+                        return "No summary available"
+                    else:
+                        return json.dumps(result_data, indent=2)
+                else:
+                    return str(result_data)
+            except json.JSONDecodeError:
+                return tool_result
+        else:
+            return str(tool_result)
+
+    def get_memory_statistics(self, files_implemented: int = 0) -> Dict[str, Any]:
+        """Get memory agent statistics"""
+        unimplemented_files = self.get_unimplemented_files()
+        return {
+            "last_write_file_detected": self.last_write_file_detected,
+            "should_clear_memory_next": self.should_clear_memory_next,
+            "current_round": self.current_round,
+            "concise_mode_active": self.should_use_concise_mode(),
+            "current_round_tool_results": len(self.current_round_tool_results),
+            "essential_tools_recorded": [
+                r["tool_name"] for r in self.current_round_tool_results
+            ],
+            "implemented_files_tracked": files_implemented,
+            "implemented_files_list": self.implemented_files.copy(),
+            "phases_parsed": len(self.phase_structure),
+            "next_steps_available": bool(self.current_next_steps.strip()),
+            "next_steps_length": len(self.current_next_steps.strip())
+            if self.current_next_steps
+            else 0,
+            # File tracking statistics
+            "total_files_in_plan": len(self.all_files_list),
+            "files_implemented_count": len(self.implemented_files),
+            "files_remaining_count": len(unimplemented_files),
+            "all_files_list": self.all_files_list.copy(),
+            "unimplemented_files_list": unimplemented_files,
+            "implementation_progress_percent": (
+                len(self.implemented_files) / len(self.all_files_list) * 100
+            )
+            if self.all_files_list
+            else 0,
+        }
+
+    def get_implemented_files(self) -> List[str]:
+        """Get list of all implemented files"""
+        return self.implemented_files.copy()
+
+    def get_all_files_list(self) -> List[str]:
+        """Get list of all files that should be implemented according to the plan"""
+        return self.all_files_list.copy()
+
+    def get_unimplemented_files(self) -> List[str]:
+        """
+        Get list of files that haven't been implemented yet
+
+        Returns:
+            List of file paths that still need to be implemented
+        """
+        implemented_set = set(self.implemented_files)
+        unimplemented = [f for f in self.all_files_list if f not in implemented_set]
+        return unimplemented
+
+    def get_formatted_files_lists(self) -> Dict[str, str]:
+        """
+        Get formatted strings for implemented and unimplemented files
+
+        Returns:
+            Dictionary with 'implemented' and 'unimplemented' formatted lists
+        """
+        implemented_list = (
+            "\n".join([f"- {file}" for file in self.implemented_files])
+            if self.implemented_files
+            else "- None yet"
+        )
+
+        unimplemented_files = self.get_unimplemented_files()
+        unimplemented_list = (
+            "\n".join([f"- {file}" for file in unimplemented_files])
+            if unimplemented_files
+            else "- All files implemented!"
+        )
+
+        return {"implemented": implemented_list, "unimplemented": unimplemented_list}
+
+    def get_current_next_steps(self) -> str:
+        """Get the current Next Steps information"""
+        return self.current_next_steps
+
+    def clear_next_steps(self):
+        """Clear the stored Next Steps information"""
+        if self.current_next_steps.strip():
+            self.logger.info("🧹 Next Steps information cleared")
+        self.current_next_steps = ""
+
+    def set_next_steps(self, next_steps: str):
+        """Manually set Next Steps information"""
+        self.current_next_steps = next_steps
+        self.logger.info(
+            f"📝 Next Steps manually set ({len(next_steps.strip())} chars)"
+        )
+
+    def should_trigger_memory_optimization(
+        self, messages: List[Dict[str, Any]], files_implemented: int = 0
+    ) -> bool:
+        """
+        Check if memory optimization should be triggered
+        NEW LOGIC: Trigger after write_file has been detected
+
+        Args:
+            messages: Current message list
+            files_implemented: Number of files implemented so far
+
+        Returns:
+            True if concise mode should be applied
+        """
+        # Trigger if we detected write_file and should clear memory
+        if self.should_clear_memory_next:
+            # self.logger.info(f"🎯 Triggering CONCISE memory optimization (write_file detected, files: {files_implemented})")
+            return True
+
+        # No optimization before any write_file
+        return False
+
+    def apply_memory_optimization(
+        self, system_prompt: str, messages: List[Dict[str, Any]], files_implemented: int
+    ) -> List[Dict[str, Any]]:
+        """
+        Apply memory optimization using concise approach
+        NEW LOGIC: Clear all history after write_file, keep only system_prompt + initial_plan + current tools
+
+        Args:
+            system_prompt: Current system prompt
+            messages: Original message list
+            files_implemented: Number of files implemented so far
+
+        Returns:
+            Optimized message list
+        """
+        if not self.should_clear_memory_next:
+            # Before any write_file, return original messages
+            return messages
+
+        # Apply concise memory optimization after write_file detection
+        # self.logger.info(f"🧹 CLEARING MEMORY after write_file - creating clean slate")
+        optimized_messages = self.create_concise_messages(
+            system_prompt, messages, files_implemented
+        )
+
+        # Clear the flag after applying optimization
+        self.should_clear_memory_next = False
+
+        compression_ratio = (
+            ((len(messages) - len(optimized_messages)) / len(messages) * 100)
+            if messages
+            else 0
+        )
+        self.logger.info(
+            f"🎯 CONCISE optimization applied: {len(messages)} → {len(optimized_messages)} messages ({compression_ratio:.1f}% compression)"
+        )
+
+        return optimized_messages
+
+    def clear_current_round_tool_results(self):
+        """Clear current round tool results (called when starting new round)"""
+        self.current_round_tool_results = []
+        self.logger.info("🧹 Current round tool results cleared")
+
+    def debug_concise_state(self, files_implemented: int = 0):
+        """Debug method to show current concise memory state"""
+        stats = self.get_memory_statistics(files_implemented)
+
+        print("=" * 60)
+        print("🎯 CONCISE MEMORY AGENT STATE (Write-File-Based)")
+        print("=" * 60)
+        print(f"Last write_file detected: {stats['last_write_file_detected']}")
+        print(f"Should clear memory next: {stats['should_clear_memory_next']}")
+        print(f"Files implemented: {stats['implemented_files_tracked']}")
+        print(f"Current round: {stats['current_round']}")
+        print(f"Concise mode active: {stats['concise_mode_active']}")
+        print(f"Current round tool results: {stats['current_round_tool_results']}")
+        print(f"Essential tools recorded: {stats['essential_tools_recorded']}")
+        print(f"Implemented files tracked: {len(self.implemented_files)}")
+        print(f"Implemented files list: {self.implemented_files}")
+        print(f"Code summary file exists: {os.path.exists(self.code_summary_path)}")
+        print(f"Next Steps available: {stats['next_steps_available']}")
+        print(f"Next Steps length: {stats['next_steps_length']} chars")
+        if self.current_next_steps.strip():
+            print(f"Next Steps preview: {self.current_next_steps[:100]}...")
+        print("")
+        print("📋 FILE TRACKING:")
+        print(f"  Total files in plan: {stats['total_files_in_plan']}")
+        print(f"  Files implemented: {stats['files_implemented_count']}")
+        print(f"  Files remaining: {stats['files_remaining_count']}")
+        print(f"  Progress: {stats['implementation_progress_percent']:.1f}%")
+        if stats["unimplemented_files_list"]:
+            print(f"  Next possible files: {stats['unimplemented_files_list'][:3]}...")
+        print("")
+        print(
+            "📊 NEW LOGIC: write_file → clear memory → accumulate tools → next write_file"
+        )
+        print("📊 NEXT STEPS: Stored separately from file, included in tool results")
+        print(
+            "📊 FILE TRACKING: All files extracted from plan, unimplemented files guide LLM decisions"
+        )
+        print("📊 Essential Tools Tracked:")
+        essential_tools = [
+            "read_code_mem",
+            "read_file",
+            "write_file",
+            "execute_python",
+            "execute_bash",
+            "search_code",
+            "search_reference_code",
+            "get_file_structure",
+        ]
+        for tool in essential_tools:
+            tool_count = sum(
+                1 for r in self.current_round_tool_results if r["tool_name"] == tool
+            )
+            print(f"  - {tool}: {tool_count} calls")
+        print("=" * 60)

projects/ui/DeepCode/workflows/agents/memory_agent_concise_multi.py

@@ -0,0 +1,1659 @@
+"""
+Concise Memory Agent for Code Implementation Workflow - Multi-File Only Support
+
+This memory agent implements a focused approach with ONLY multi-file capabilities:
+1. Before first batch: Normal conversation flow
+2. After first batch: Keep only system_prompt + initial_plan + current round tool results
+3. Clean slate for each new code batch generation
+4. MULTI-FILE ONLY: Support for summarizing multiple files simultaneously (max 5)
+
+Key Features:
+- Preserves system prompt and initial plan always
+- After first batch generation, discards previous conversation history
+- Keeps only current round tool results from essential tools:
+  * read_multiple_files, write_multiple_files
+  * execute_python, execute_bash
+  * search_code, search_reference_code, get_file_structure
+- Provides clean, focused input for next write_multiple_files operation
+- MULTI-FILE ONLY: No single file support
+- FILE TRACKING: Gets ALL file information from workflow, no internal tracking
+"""
+
+import json
+import logging
+import os
+import time
+from datetime import datetime
+from typing import Dict, Any, List, Optional
+
+
+class ConciseMemoryAgent:
+    """
+    Concise Memory Agent - Focused Information Retention with MULTI-FILE ONLY Support
+
+    Core Philosophy:
+    - Preserve essential context (system prompt + initial plan)
+    - After first batch generation, use clean slate approach
+    - Keep only current round tool results from multi-file MCP tools
+    - Remove conversational clutter and previous tool calls
+    - MULTI-FILE ONLY: Support for multiple file implementations in single operation
+    - FILE TRACKING: Receives ALL file information from workflow (no internal tracking)
+
+    Essential Tools Tracked:
+    - Multi-File Operations: read_multiple_files, write_multiple_files
+    - Code Analysis: search_code, search_reference_code, get_file_structure
+    - Execution: execute_python, execute_bash
+    """
+
+    def __init__(
+        self,
+        initial_plan_content: str,
+        logger: Optional[logging.Logger] = None,
+        target_directory: Optional[str] = None,
+        default_models: Optional[Dict[str, str]] = None,
+        max_files_per_batch: int = 3,
+    ):
+        """
+        Initialize Concise Memory Agent with MULTI-FILE ONLY support
+
+        Args:
+            initial_plan_content: Content of initial_plan.txt
+            logger: Logger instance
+            target_directory: Target directory for saving summaries
+            default_models: Default models configuration from workflow
+            max_files_per_batch: Maximum number of files to implement simultaneously (default: 3)
+        """
+        self.logger = logger or self._create_default_logger()
+        self.initial_plan = initial_plan_content
+        self.max_files_per_batch = max_files_per_batch
+
+        # Store default models configuration
+        self.default_models = default_models or {
+            "anthropic": "claude-sonnet-4-20250514",
+            "openai": "gpt-4o",
+        }
+
+        # Memory state tracking - new logic: trigger after each write_multiple_files
+        self.last_write_multiple_files_detected = (
+            False  # Track if write_multiple_files was called in current iteration
+        )
+        self.should_clear_memory_next = False  # Flag to clear memory in next round
+        self.current_round = 0
+
+        # self.phase_structure = self._parse_phase_structure()
+
+        # Memory configuration
+        if target_directory:
+            self.save_path = target_directory
+        else:
+            self.save_path = "./deepcode_lab/papers/1/"
+
+        # Code summary file path
+        self.code_summary_path = os.path.join(
+            self.save_path, "implement_code_summary.md"
+        )
+
+        # Current round tool results storage
+        self.current_round_tool_results = []
+
+        self.logger.info(
+            f"Concise Memory Agent initialized with target directory: {self.save_path}"
+        )
+        self.logger.info(f"Code summary will be saved to: {self.code_summary_path}")
+        self.logger.info(f"Max files per batch: {self.max_files_per_batch}")
+        self.logger.info(
+            "📝 MULTI-FILE LOGIC: Memory clearing triggered after each write_multiple_files call"
+        )
+        self.logger.info(
+            "🆕 MULTI-FILE ONLY: No single file support - batch operations only"
+        )
+        self.logger.info(
+            "📊 FILE TRACKING: ALL file information received from workflow (no internal tracking)"
+        )
+
+    def _create_default_logger(self) -> logging.Logger:
+        """Create default logger"""
+        logger = logging.getLogger(f"{__name__}.ConciseMemoryAgent")
+        logger.setLevel(logging.INFO)
+        return logger
+
+    async def create_multi_code_implementation_summary(
+        self,
+        client,
+        client_type: str,
+        file_implementations: Dict[str, str],
+        files_implemented: int,
+        implemented_files: List[str],  # Receive from workflow
+    ) -> str:
+        """
+        Create LLM-based code implementation summary for multiple files
+        ONLY AVAILABLE METHOD: Handles multiple files simultaneously with separate summaries for each
+
+        Args:
+            client: LLM client instance
+            client_type: Type of LLM client ("anthropic" or "openai")
+            file_implementations: Dictionary mapping file_path to implementation_content
+            files_implemented: Number of files implemented so far
+            implemented_files: List of all implemented files (from workflow)
+
+        Returns:
+            LLM-generated formatted code implementation summaries for all files
+        """
+        try:
+            # Validate input
+            if not file_implementations:
+                raise ValueError("No file implementations provided")
+
+            if len(file_implementations) > self.max_files_per_batch:
+                raise ValueError(
+                    f"Too many files provided ({len(file_implementations)}), max is {self.max_files_per_batch}"
+                )
+
+            # Create prompt for LLM summary of multiple files
+            summary_prompt = self._create_multi_code_summary_prompt(
+                file_implementations, files_implemented, implemented_files
+            )
+            summary_messages = [{"role": "user", "content": summary_prompt}]
+
+            # Get LLM-generated summary
+            llm_response = await self._call_llm_for_summary(
+                client, client_type, summary_messages
+            )
+            llm_summary = llm_response.get("content", "")
+
+            # Extract sections for each file and next steps
+            multi_sections = self._extract_multi_summary_sections(
+                llm_summary, file_implementations.keys()
+            )
+
+            # Format and save summary for each file (WITHOUT Next Steps)
+            all_formatted_summaries = []
+
+            for file_path in file_implementations.keys():
+                file_sections = multi_sections.get("files", {}).get(file_path, {})
+
+                # Format summary with ONLY Implementation Progress and Dependencies for file saving
+                file_summary_content = ""
+                if file_sections.get("core_purpose"):
+                    file_summary_content += file_sections["core_purpose"] + "\n\n"
+                if file_sections.get("public_interface"):
+                    file_summary_content += file_sections["public_interface"] + "\n\n"
+                if file_sections.get("internal_dependencies"):
+                    file_summary_content += (
+                        file_sections["internal_dependencies"] + "\n\n"
+                    )
+                if file_sections.get("external_dependencies"):
+                    file_summary_content += (
+                        file_sections["external_dependencies"] + "\n\n"
+                    )
+                if file_sections.get("implementation_notes"):
+                    file_summary_content += (
+                        file_sections["implementation_notes"] + "\n\n"
+                    )
+
+                # Create the formatted summary for file saving (WITHOUT Next Steps)
+                formatted_summary = self._format_code_implementation_summary(
+                    file_path, file_summary_content.strip(), files_implemented
+                )
+
+                all_formatted_summaries.append(formatted_summary)
+
+                # Save to implement_code_summary.md (append mode) - ONLY Implementation Progress and Dependencies
+                await self._save_code_summary_to_file(formatted_summary, file_path)
+
+            # Combine all summaries for return
+            combined_summary = "\n".join(all_formatted_summaries)
+
+            self.logger.info(
+                f"Created and saved multi-file code summaries for {len(file_implementations)} files"
+            )
+
+            return combined_summary
+
+        except Exception as e:
+            self.logger.error(
+                f"Failed to create LLM-based multi-file code implementation summary: {e}"
+            )
+            # Fallback to simple summary for each file
+            return self._create_fallback_multi_code_summary(
+                file_implementations, files_implemented
+            )
+
+    def _create_multi_code_summary_prompt(
+        self,
+        file_implementations: Dict[str, str],
+        files_implemented: int,
+        implemented_files: List[str],
+    ) -> str:
+        """
+        Create prompt for LLM to generate multi-file code implementation summary
+
+        Args:
+            file_implementations: Dictionary mapping file_path to implementation_content
+            files_implemented: Number of files implemented so far
+            implemented_files: List of all implemented files (from workflow)
+
+        Returns:
+            Prompt for LLM multi-file summarization
+        """
+
+        # Format file lists using workflow data
+        implemented_files_list = (
+            "\n".join([f"- {file}" for file in implemented_files])
+            if implemented_files
+            else "- None yet"
+        )
+
+        # Note: We don't have unimplemented files list anymore - workflow will provide when needed
+
+        # Format file implementations for the prompt
+        implementation_sections = []
+        for file_path, content in file_implementations.items():
+            implementation_sections.append(f"""
+            **File: {file_path}**
+            {content}
+            """)
+
+        files_list = list(file_implementations.keys())
+        files_count = len(files_list)
+
+        prompt = f"""You are an expert code implementation summarizer. Analyze the {files_count} implemented code files and create structured summaries for each.
+
+**All Previously Implemented Files:**
+{implemented_files_list}
+
+**Current Implementation Context:**
+- **Files Implemented**: {', '.join(files_list)}
+- **Total Files Implemented**: {files_implemented}
+- **Files in This Batch**: {files_count}
+
+**Initial Plan Reference:**
+{self.initial_plan[:]}
+
+**Implemented Code Content:**
+{''.join(implementation_sections)}
+
+**Required Summary Format:**
+
+**FOR EACH FILE, provide separate sections:**
+
+**File: {{file_path}}**
+**Core Purpose** (provide a general overview of the file's main responsibility):
+- {{1-2 sentence description of file's main responsibility}}
+
+**Public Interface** (what other files can use, if any):
+- Class {{ClassName}}: {{purpose}} | Key methods: {{method_names}} | Constructor params: {{params}}
+- Function {{function_name}}({{params}}): {{purpose}} -> {{return_type}}: {{purpose}}
+- Constants/Types: {{name}}: {{value/description}}
+
+**Internal Dependencies** (what this file imports/requires, if any):
+- From {{module/file}}: {{specific_imports}}
+- External packages: {{package_name}} - {{usage_context}}
+
+**External Dependencies** (what depends on this file, if any):
+- Expected to be imported by: {{likely_consumer_files}}
+- Key exports used elsewhere: {{main_interfaces}}
+
+**Implementation Notes**: (if any)
+- Architecture decisions: {{key_choices_made}}
+- Cross-File Relationships: {{how_files_work_together}}
+
+[Repeat for all {files_count} files...]
+
+**Instructions:**
+- Provide separate Implementation Progress and Dependencies sections for each of the {files_count} files
+- Be precise and concise for each file
+- Focus on function interfaces that other files will need
+- Extract actual function signatures from the code
+- Use the exact format specified above
+
+**Summary:**"""
+
+        return prompt
+
+    def _extract_multi_summary_sections(
+        self, llm_summary: str, file_paths: List[str]
+    ) -> Dict[str, Any]:
+        """
+        Extract different sections from LLM-generated multi-file summary
+        """
+        result = {
+            "files": {},
+        }
+
+        try:
+            # Convert dict_keys to list if needed
+            if hasattr(file_paths, "keys"):
+                file_paths = list(file_paths)
+            elif not isinstance(file_paths, list):
+                file_paths = list(file_paths)
+
+            lines = llm_summary.split("\n")
+            current_file = None
+            current_section = None
+            current_content = []
+            file_sections = {}
+
+            for i, line in enumerate(lines):
+                line_lower = line.lower().strip()
+                original_line = line.strip()
+
+                # Skip empty lines
+                if not original_line:
+                    if current_section:
+                        current_content.append(line)
+                    continue
+
+                # File header detection
+                if (
+                    "**file:" in line_lower or "file:" in line_lower
+                ) and "**" in original_line:
+                    # Save previous section
+                    if current_file and current_section and current_content:
+                        if current_file not in file_sections:
+                            file_sections[current_file] = {}
+                        file_sections[current_file][current_section] = "\n".join(
+                            current_content
+                        ).strip()
+
+                    # Extract file path
+                    file_header = original_line.lower()
+                    if "**file:" in file_header:
+                        file_header = original_line[
+                            original_line.lower().find("file:") + 5 :
+                        ]
+                        if "**" in file_header:
+                            file_header = file_header[: file_header.find("**")]
+                    else:
+                        file_header = original_line[
+                            original_line.lower().find("file:") + 5 :
+                        ]
+
+                    file_header = file_header.strip()
+                    current_file = None
+
+                    # File matching
+                    for file_path in file_paths:
+                        file_name = file_path.split("/")[-1]
+                        if (
+                            file_path in file_header
+                            or file_header in file_path
+                            or file_name in file_header
+                            or file_header in file_name
+                        ):
+                            current_file = file_path
+                            break
+
+                    current_section = None
+                    current_content = []
+                    continue
+
+                # Section detection within files
+                if current_file:
+                    section_matched = False
+
+                    if "core purpose" in line_lower and "**" in original_line:
+                        if current_section and current_content:
+                            if current_file not in file_sections:
+                                file_sections[current_file] = {}
+                            file_sections[current_file][current_section] = "\n".join(
+                                current_content
+                            ).strip()
+                        current_section = "core_purpose"
+                        current_content = []
+                        section_matched = True
+                    elif "public interface" in line_lower and "**" in original_line:
+                        if current_section and current_content:
+                            if current_file not in file_sections:
+                                file_sections[current_file] = {}
+                            file_sections[current_file][current_section] = "\n".join(
+                                current_content
+                            ).strip()
+                        current_section = "public_interface"
+                        current_content = []
+                        section_matched = True
+                    elif (
+                        "internal dependencies" in line_lower and "**" in original_line
+                    ):
+                        if current_section and current_content:
+                            if current_file not in file_sections:
+                                file_sections[current_file] = {}
+                            file_sections[current_file][current_section] = "\n".join(
+                                current_content
+                            ).strip()
+                        current_section = "internal_dependencies"
+                        current_content = []
+                        section_matched = True
+                    elif (
+                        "external dependencies" in line_lower and "**" in original_line
+                    ):
+                        if current_section and current_content:
+                            if current_file not in file_sections:
+                                file_sections[current_file] = {}
+                            file_sections[current_file][current_section] = "\n".join(
+                                current_content
+                            ).strip()
+                        current_section = "external_dependencies"
+                        current_content = []
+                        section_matched = True
+                    elif "implementation notes" in line_lower and "**" in original_line:
+                        if current_section and current_content:
+                            if current_file not in file_sections:
+                                file_sections[current_file] = {}
+                            file_sections[current_file][current_section] = "\n".join(
+                                current_content
+                            ).strip()
+                        current_section = "implementation_notes"
+                        current_content = []
+                        section_matched = True
+
+                    # If no section header matched, add to current content
+                    if not section_matched and current_section:
+                        current_content.append(line)
+
+            # Save the final section
+            if current_file and current_section and current_content:
+                if current_file not in file_sections:
+                    file_sections[current_file] = {}
+                file_sections[current_file][current_section] = "\n".join(
+                    current_content
+                ).strip()
+
+            # Build final result
+            for file_path in file_paths:
+                sections = file_sections.get(file_path, {})
+                result["files"][file_path] = {}
+                if "core_purpose" in sections:
+                    result["files"][file_path]["core_purpose"] = (
+                        "**Core Purpose**:\n" + sections["core_purpose"]
+                    )
+                if "public_interface" in sections:
+                    result["files"][file_path]["public_interface"] = (
+                        "**Public Interface**:\n" + sections["public_interface"]
+                    )
+                if "implementation_notes" in sections:
+                    result["files"][file_path]["implementation_notes"] = (
+                        "**Implementation Notes**:\n" + sections["implementation_notes"]
+                    )
+                if "internal_dependencies" in sections:
+                    result["files"][file_path]["internal_dependencies"] = (
+                        "**Internal Dependencies**:\n"
+                        + sections["internal_dependencies"]
+                    )
+                if "external_dependencies" in sections:
+                    result["files"][file_path]["external_dependencies"] = (
+                        "**External Dependencies**:\n"
+                        + sections["external_dependencies"]
+                    )
+
+            self.logger.info(
+                f"📋 Extracted multi-file sections for {len(result['files'])} files"
+            )
+
+        except Exception as e:
+            self.logger.error(f"Failed to extract multi-file summary sections: {e}")
+            self.logger.error(f"📋 file_paths type: {type(file_paths)}")
+            self.logger.error(f"📋 file_paths value: {file_paths}")
+            self.logger.error(f"📋 file_paths length: {len(file_paths)}")
+            for file_path in file_paths:
+                result["files"][file_path] = {
+                    "core_purpose": f"**Core Purpose**: {file_path} completed.",
+                    "public_interface": "**Public Interface**: Public interface need manual review.",
+                    "internal_dependencies": "**Internal Dependencies**: Internal dependencies need manual review.",
+                    "external_dependencies": "**External Dependencies**: External dependencies need manual review.",
+                    "implementation_notes": "**Implementation Notes**: Implementation notes need manual review.",
+                }
+
+        return result
+
+    def _format_code_implementation_summary(
+        self, file_path: str, llm_summary: str, files_implemented: int
+    ) -> str:
+        """
+        Format the LLM-generated summary into the final structure
+
+        Args:
+            file_path: Path of the implemented file
+            llm_summary: LLM-generated summary content
+            files_implemented: Number of files implemented so far
+
+        Returns:
+            Formatted summary
+        """
+        timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+
+        formatted_summary = f"""# Code Implementation Summary
+**Generated**: {timestamp}
+**File Implemented**: {file_path}
+
+{llm_summary}
+
+---
+*Auto-generated by Memory Agent*
+"""
+        return formatted_summary
+
+    def _create_fallback_multi_code_summary(
+        self, file_implementations: Dict[str, str], files_implemented: int
+    ) -> str:
+        """
+        Create fallback multi-file summary when LLM is unavailable
+
+        Args:
+            file_implementations: Dictionary mapping file_path to implementation_content
+            files_implemented: Number of files implemented so far
+
+        Returns:
+            Fallback multi-file summary
+        """
+        # Create fallback summaries for each file
+        fallback_summaries = []
+        timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+
+        for file_path in file_implementations.keys():
+            fallback_summary = f"""# Code Implementation Summary
+**Generated**: {timestamp}
+**File Implemented**: {file_path}
+**Multi-file batch summary failed to generate.**
+
+---
+*Auto-generated by Concise Memory Agent (Multi-File Fallback Mode)*
+"""
+            fallback_summaries.append(fallback_summary)
+
+        return "\n".join(fallback_summaries)
+
+    async def _save_code_summary_to_file(self, new_summary: str, file_path: str):
+        """
+        Append code implementation summary to implement_code_summary.md
+        Accumulates all implementations with clear separators
+
+        Args:
+            new_summary: New summary content to append
+            file_path: Path of the file for which the summary was generated
+        """
+        try:
+            # Create directory if it doesn't exist
+            os.makedirs(os.path.dirname(self.code_summary_path), exist_ok=True)
+
+            # Check if file exists to determine if we need header
+            file_exists = os.path.exists(self.code_summary_path)
+
+            # Open in append mode to accumulate all implementations
+            with open(self.code_summary_path, "a", encoding="utf-8") as f:
+                if not file_exists:
+                    # Write header for new file
+                    f.write("# Code Implementation Progress Summary\n")
+                    f.write("*Accumulated implementation progress for all files*\n\n")
+
+                # Add clear separator between implementations
+                f.write("\n" + "=" * 80 + "\n")
+                f.write(f"## IMPLEMENTATION File {file_path}\n")
+                f.write("=" * 80 + "\n\n")
+
+                # Write the new summary
+                f.write(new_summary)
+                f.write("\n\n")
+
+            self.logger.info(
+                f"Appended LLM-based code implementation summary to: {self.code_summary_path}"
+            )
+
+        except Exception as e:
+            self.logger.error(f"Failed to save code implementation summary: {e}")
+
+    async def _call_llm_for_summary(
+        self, client, client_type: str, summary_messages: List[Dict]
+    ) -> Dict[str, Any]:
+        """
+        Call LLM for code implementation summary generation ONLY
+
+        This method is used only for creating code implementation summaries,
+        NOT for conversation summarization which has been removed.
+        """
+        if client_type == "anthropic":
+            response = await client.messages.create(
+                model=self.default_models["anthropic"],
+                system="You are an expert code implementation summarizer. Create structured summaries of implemented code files that preserve essential information about functions, dependencies, and implementation approaches.",
+                messages=summary_messages,
+                max_tokens=8000,  # Increased for multi-file support
+                temperature=0.2,
+            )
+
+            content = ""
+            for block in response.content:
+                if block.type == "text":
+                    content += block.text
+
+            return {"content": content}
+
+        elif client_type == "openai":
+            openai_messages = [
+                {
+                    "role": "system",
+                    "content": "You are an expert code implementation summarizer. Create structured summaries of implemented code files that preserve essential information about functions, dependencies, and implementation approaches.",
+                }
+            ]
+            openai_messages.extend(summary_messages)
+
+            # Try max_tokens and temperature first, fallback to max_completion_tokens without temperature if unsupported
+            try:
+                response = await client.chat.completions.create(
+                    model=self.default_models["openai"],
+                    messages=openai_messages,
+                    max_tokens=8000,  # Increased for multi-file support
+                    temperature=0.2,
+                )
+            except Exception as e:
+                if "max_tokens" in str(e) and "max_completion_tokens" in str(e):
+                    # Retry with max_completion_tokens and no temperature for models that require it
+                    response = await client.chat.completions.create(
+                        model=self.default_models["openai"],
+                        messages=openai_messages,
+                        max_completion_tokens=8000,  # Increased for multi-file support
+                    )
+                else:
+                    raise
+
+            return {"content": response.choices[0].message.content or ""}
+
+        else:
+            raise ValueError(f"Unsupported client type: {client_type}")
+
+    def start_new_round(self, iteration: Optional[int] = None):
+        """Start a new dialogue round and reset tool results
+
+        Args:
+            iteration: Optional iteration number from workflow to sync with current_round
+        """
+        if iteration is not None:
+            # Sync with workflow iteration
+            self.current_round = iteration
+        else:
+            # Default behavior: increment round counter
+            self.current_round += 1
+            self.logger.info(f"🔄 Started new round {self.current_round}")
+
+        self.current_round_tool_results = []  # Clear previous round results
+
+    def record_tool_result(
+        self, tool_name: str, tool_input: Dict[str, Any], tool_result: Any
+    ):
+        """
+        Record tool result for current round and detect write_multiple_files calls
+
+        Args:
+            tool_name: Name of the tool called
+            tool_input: Input parameters for the tool
+            tool_result: Result returned by the tool
+        """
+        # Detect write_multiple_files calls to trigger memory clearing
+        if tool_name == "write_multiple_files":
+            self.last_write_multiple_files_detected = True
+            self.should_clear_memory_next = True
+
+        # Only record specific tools that provide essential information
+        essential_tools = [
+            "read_multiple_files",  # Read multiple file contents
+            "write_multiple_files",  # Write multiple file contents (important for tracking implementations)
+            "execute_python",  # Execute Python code (for testing/validation)
+            "execute_bash",  # Execute bash commands (for build/execution)
+            "search_code",  # Search code patterns
+            "search_reference_code",  # Search reference code (if available)
+            "get_file_structure",  # Get file structure (for understanding project layout)
+        ]
+
+        if tool_name in essential_tools:
+            tool_record = {
+                "tool_name": tool_name,
+                "tool_input": tool_input,
+                "tool_result": tool_result,
+                "timestamp": time.time(),
+            }
+            self.current_round_tool_results.append(tool_record)
+
+    def should_use_concise_mode(self) -> bool:
+        """
+        Check if concise memory mode should be used
+
+        Returns:
+            True if first batch has been generated and concise mode should be active
+        """
+        return self.last_write_multiple_files_detected
+
+    def create_concise_messages_revise(
+        self,
+        system_prompt: str,
+        messages: List[Dict[str, Any]],
+        files_implemented: int,
+        task_description: str,
+        file_batch: List[str],
+        is_first_batch: bool = True,
+        implemented_files: List[str] = None,  # Receive from workflow
+        all_files: List[str] = None,  # NEW: Receive all files from workflow
+    ) -> List[Dict[str, Any]]:
+        """
+        Create concise message list for LLM input specifically for revision execution
+        ALIGNED with _execute_multi_file_batch_revision in code_evaluation_workflow
+
+        Args:
+            system_prompt: Current system prompt
+            messages: Original message list
+            files_implemented: Number of files implemented so far
+            task_description: Description of the current task
+            file_batch: Files to implement in this batch
+            is_first_batch: Whether this is the first batch (use file_batch) or subsequent
+            implemented_files: List of all implemented files (from workflow)
+            all_files: List of all files that should be implemented (from workflow)
+
+        Returns:
+            Concise message list containing only essential information for revision
+        """
+        # Use empty lists if not provided
+        if implemented_files is None:
+            implemented_files = []
+        if all_files is None:
+            all_files = []
+
+        self.logger.info(
+            "🎯 Using CONCISE memory mode for revision - Clear slate after write_multiple_files"
+        )
+
+        concise_messages = []
+
+        # Format file lists using workflow data
+        implemented_files_list = (
+            "\n".join([f"- {file}" for file in implemented_files])
+            if implemented_files
+            else "- None yet"
+        )
+
+        # Calculate unimplemented files from workflow data
+
+        # Read initial plan and memory content
+        initial_plan_content = self.initial_plan
+        memory_content = (
+            self._read_code_knowledge_base()
+            or "No previous implementation memory available"
+        )
+
+        files_to_implement = file_batch
+        file_list = "\n".join([f"- {file_path}" for file_path in files_to_implement])
+
+        # Create revision-specific task message
+        task_message = f"""Task: {task_description}
+
+    Files to implement in this batch ({len(files_to_implement)} files):
+    {file_list}
+
+    MANDATORY JSON FORMAT REQUIREMENTS:
+    1. Use write_multiple_files tool
+    2. Parameter name: "file_implementations"
+    3. Value must be a VALID JSON string with ESCAPED newlines
+    4. Use \\n for newlines, \\t for tabs, \\" for quotes
+    5. NO literal newlines in the JSON string
+
+    CORRECT JSON FORMAT EXAMPLE:
+    {{
+    "file1.py": "# Comment\\nclass MyClass:\\n    def __init__(self):\\n        pass\\n",
+    "file2.py": "import os\\n\\ndef main():\\n    print('Hello')\\n"
+    }}
+
+    Initial Implementation Plan Context:
+    {initial_plan_content}
+
+    Previous Implementation Memory:
+    {memory_content}
+
+    **All Previously Implemented Files:**
+    {implemented_files_list}
+
+    **Current Status:** {files_implemented} files implemented
+
+    IMPLEMENTATION REQUIREMENTS:
+    - Create functional code for each file
+    - Use proper Python syntax and imports
+    - Include docstrings and comments
+    - Follow the existing patterns from memory
+
+    Files to implement: {files_to_implement}
+
+    Call write_multiple_files NOW with PROPERLY ESCAPED JSON containing all {len(files_to_implement)} files."""
+
+        concise_messages.append({"role": "user", "content": task_message})
+
+        # Debug output for files to implement
+        print("✅ Files to implement:")
+        for file_path in files_to_implement:
+            print(f"{file_path}")
+
+        return concise_messages
+
+    def _calculate_message_statistics(
+        self, messages: List[Dict[str, Any]], label: str
+    ) -> Dict[str, Any]:
+        """
+        Calculate statistics for a message list
+
+        Args:
+            messages: List of messages to analyze
+            label: Label for logging
+
+        Returns:
+            Dictionary with statistics
+        """
+        total_chars = 0
+        total_words = 0
+
+        for msg in messages:
+            content = msg.get("content", "")
+            total_chars += len(content)
+            total_words += len(content.split())
+
+        # Estimate tokens (rough approximation: ~4 characters per token)
+        estimated_tokens = total_chars // 4
+
+        stats = {
+            "message_count": len(messages),
+            "total_characters": total_chars,
+            "total_words": total_words,
+            "estimated_tokens": estimated_tokens,
+            "summary": f"{len(messages)} msgs, {total_chars:,} chars, ~{estimated_tokens:,} tokens",
+        }
+
+        return stats
+
+    def _calculate_memory_savings(
+        self, original_stats: Dict[str, Any], optimized_stats: Dict[str, Any]
+    ) -> Dict[str, Any]:
+        """
+        Calculate memory savings between original and optimized messages
+
+        Args:
+            original_stats: Statistics for original messages
+            optimized_stats: Statistics for optimized messages
+
+        Returns:
+            Dictionary with savings calculations
+        """
+        messages_saved = (
+            original_stats["message_count"] - optimized_stats["message_count"]
+        )
+        chars_saved = (
+            original_stats["total_characters"] - optimized_stats["total_characters"]
+        )
+        tokens_saved_estimate = (
+            original_stats["estimated_tokens"] - optimized_stats["estimated_tokens"]
+        )
+
+        # Calculate percentages (avoid division by zero)
+        messages_saved_percent = (
+            messages_saved / max(original_stats["message_count"], 1)
+        ) * 100
+        chars_saved_percent = (
+            chars_saved / max(original_stats["total_characters"], 1)
+        ) * 100
+        tokens_saved_percent = (
+            tokens_saved_estimate / max(original_stats["estimated_tokens"], 1)
+        ) * 100
+
+        return {
+            "messages_saved": messages_saved,
+            "chars_saved": chars_saved,
+            "tokens_saved_estimate": tokens_saved_estimate,
+            "messages_saved_percent": messages_saved_percent,
+            "chars_saved_percent": chars_saved_percent,
+            "tokens_saved_percent": tokens_saved_percent,
+        }
+
+    def _read_code_knowledge_base(self) -> Optional[str]:
+        """
+        Read the implement_code_summary.md file as code knowledge base
+        Returns only the final/latest implementation entry, not all historical entries
+
+        Returns:
+            Content of the latest implementation entry if it exists, None otherwise
+        """
+        try:
+            if os.path.exists(self.code_summary_path):
+                with open(self.code_summary_path, "r", encoding="utf-8") as f:
+                    content = f.read().strip()
+                return content
+            else:
+                return None
+
+        except Exception as e:
+            self.logger.error(f"Failed to read code knowledge base: {e}")
+            return None
+
+    def _extract_latest_implementation_entry(self, content: str) -> Optional[str]:
+        """
+        Extract the latest/final implementation entry from the implement_code_summary.md content
+        Uses a simpler approach to find the last implementation section
+
+        Args:
+            content: Full content of implement_code_summary.md
+
+        Returns:
+            Latest implementation entry content, or None if not found
+        """
+        try:
+            import re
+
+            # Pattern to match the start of implementation sections
+            section_pattern = r"={80}\s*\n## IMPLEMENTATION File .+?"
+
+            # Find all implementation section starts
+            matches = list(re.finditer(section_pattern, content))
+
+            if not matches:
+                # No implementation sections found
+                lines = content.split("\n")
+                fallback_content = (
+                    "\n".join(lines[:10]) + "\n... (truncated for brevity)"
+                    if len(lines) > 10
+                    else content
+                )
+                self.logger.info(
+                    "📖 No implementation sections found, using fallback content"
+                )
+                return fallback_content
+
+            # Get the start position of the last implementation section
+            last_match = matches[-1]
+            start_pos = last_match.start()
+
+            # Take everything from the last section start to the end of content
+            latest_entry = content[start_pos:].strip()
+
+            return latest_entry
+
+        except Exception as e:
+            self.logger.error(f"Failed to extract latest implementation entry: {e}")
+            # Return last 1000 characters as fallback
+            return content[-500:] if len(content) > 500 else content
+
+    def _format_tool_results(self) -> str:
+        """
+        Format current round tool results for LLM input
+
+        Returns:
+            Formatted string of tool results
+        """
+        if not self.current_round_tool_results:
+            return "No tool results in current round."
+
+        formatted_results = []
+
+        for result in self.current_round_tool_results:
+            tool_name = result["tool_name"]
+            tool_input = result["tool_input"]
+            tool_result = result["tool_result"]
+
+            # Format based on tool type
+            if tool_name == "read_multiple_files":
+                file_requests = tool_input.get("file_requests", "unknown")
+                formatted_results.append(f"""
+**read_multiple_files Result for {file_requests}:**
+{self._format_tool_result_content(tool_result)}
+""")
+            elif tool_name == "write_multiple_files":
+                formatted_results.append(f"""
+**write_multiple_files Result for batch:**
+{self._format_tool_result_content(tool_result)}
+""")
+            elif tool_name == "execute_python":
+                code_snippet = (
+                    tool_input.get("code", "")[:50] + "..."
+                    if len(tool_input.get("code", "")) > 50
+                    else tool_input.get("code", "")
+                )
+                formatted_results.append(f"""
+**execute_python Result (code: {code_snippet}):**
+{self._format_tool_result_content(tool_result)}
+""")
+            elif tool_name == "execute_bash":
+                command = tool_input.get("command", "unknown")
+                formatted_results.append(f"""
+**execute_bash Result (command: {command}):**
+{self._format_tool_result_content(tool_result)}
+""")
+            elif tool_name == "search_code":
+                pattern = tool_input.get("pattern", "unknown")
+                file_pattern = tool_input.get("file_pattern", "")
+                formatted_results.append(f"""
+**search_code Result (pattern: {pattern}, files: {file_pattern}):**
+{self._format_tool_result_content(tool_result)}
+""")
+            elif tool_name == "search_reference_code":
+                target_file = tool_input.get("target_file", "unknown")
+                keywords = tool_input.get("keywords", "")
+                formatted_results.append(f"""
+**search_reference_code Result for {target_file} (keywords: {keywords}):**
+{self._format_tool_result_content(tool_result)}
+""")
+            elif tool_name == "get_file_structure":
+                directory = tool_input.get(
+                    "directory_path", tool_input.get("path", "current")
+                )
+                formatted_results.append(f"""
+**get_file_structure Result for {directory}:**
+{self._format_tool_result_content(tool_result)}
+""")
+
+        return "\n".join(formatted_results)
+
+    def _format_tool_result_content(self, tool_result: Any) -> str:
+        """
+        Format tool result content for display
+
+        Args:
+            tool_result: Tool result to format
+
+        Returns:
+            Formatted string representation
+        """
+        if isinstance(tool_result, str):
+            # Try to parse as JSON for better formatting
+            try:
+                result_data = json.loads(tool_result)
+                if isinstance(result_data, dict):
+                    # Format key information
+                    if result_data.get("status") == "success":
+                        return json.dumps(result_data, indent=2)
+                    else:
+                        return json.dumps(result_data, indent=2)
+                else:
+                    return str(result_data)
+            except json.JSONDecodeError:
+                return tool_result
+        else:
+            return str(tool_result)
+
+    def get_memory_statistics(
+        self, all_files: List[str] = None, implemented_files: List[str] = None
+    ) -> Dict[str, Any]:
+        """
+        Get memory agent statistics for multi-file operations
+
+        Args:
+            all_files: List of all files that should be implemented (from workflow)
+            implemented_files: List of all implemented files (from workflow)
+        """
+        if all_files is None:
+            all_files = []
+        if implemented_files is None:
+            implemented_files = []
+
+        # Calculate unimplemented files from workflow data
+        unimplemented_files = [f for f in all_files if f not in implemented_files]
+
+        return {
+            "last_write_multiple_files_detected": self.last_write_multiple_files_detected,
+            "should_clear_memory_next": self.should_clear_memory_next,
+            "current_round": self.current_round,
+            "concise_mode_active": self.should_use_concise_mode(),
+            "current_round_tool_results": len(self.current_round_tool_results),
+            "essential_tools_recorded": [
+                r["tool_name"] for r in self.current_round_tool_results
+            ],
+            # File tracking statistics (from workflow)
+            "total_files_in_plan": len(all_files),
+            "files_implemented_count": len(implemented_files),
+            "files_remaining_count": len(unimplemented_files),
+            "all_files_list": all_files.copy(),
+            "implemented_files_list": implemented_files.copy(),
+            "unimplemented_files_list": unimplemented_files,
+            "implementation_progress_percent": (
+                len(implemented_files) / len(all_files) * 100
+            )
+            if all_files
+            else 0,
+            # Multi-file support statistics
+            "max_files_per_batch": self.max_files_per_batch,
+            "multi_file_support": True,
+            "single_file_support": False,  # Explicitly disabled
+        }
+
+    def record_multi_file_implementation(self, file_implementations: Dict[str, str]):
+        """
+        Record multi-file implementation (for compatibility with workflow)
+        NOTE: This method doesn't track files internally - workflow manages file tracking
+
+        Args:
+            file_implementations: Dictionary mapping file_path to implementation_content
+        """
+        self.logger.info(
+            f"📝 Recorded multi-file implementation batch: {len(file_implementations)} files"
+        )
+        # Note: We don't track files internally anymore - workflow handles this
+
+    # ===== ENHANCED MEMORY SYNCHRONIZATION METHODS (Phase 4+) =====
+
+    async def synchronize_revised_file_memory(
+        self,
+        client,
+        client_type: str,
+        revised_file_path: str,
+        diff_content: str,
+        new_content: str,
+        revision_type: str = "targeted_fix",
+    ) -> str:
+        """
+        Synchronize memory for a single revised file with diff information
+
+        Args:
+            client: LLM client instance
+            client_type: Type of LLM client ("anthropic" or "openai")
+            revised_file_path: Path of the revised file
+            diff_content: Unified diff showing changes made
+            new_content: Complete new content of the file
+            revision_type: Type of revision ("targeted_fix", "comprehensive_revision", etc.)
+
+        Returns:
+            Updated memory summary for the revised file
+        """
+        try:
+            self.logger.info(
+                f"🔄 Synchronizing memory for revised file: {revised_file_path}"
+            )
+
+            # Create revision-specific summary prompt
+            revision_prompt = self._create_file_revision_summary_prompt(
+                revised_file_path, diff_content, new_content, revision_type
+            )
+
+            summary_messages = [{"role": "user", "content": revision_prompt}]
+
+            # Get LLM-generated revision summary
+            llm_response = await self._call_llm_for_summary(
+                client, client_type, summary_messages
+            )
+            llm_summary = llm_response.get("content", "")
+
+            # Extract summary sections
+            revision_sections = self._extract_revision_summary_sections(llm_summary)
+
+            # Format revision summary
+            formatted_summary = self._format_file_revision_summary(
+                revised_file_path, revision_sections, diff_content, revision_type
+            )
+
+            # Save the revision summary (replace old summary)
+            await self._save_revised_file_summary(formatted_summary, revised_file_path)
+
+            self.logger.info(
+                f"✅ Memory synchronized for revised file: {revised_file_path}"
+            )
+
+            return formatted_summary
+
+        except Exception as e:
+            self.logger.error(
+                f"Failed to synchronize memory for revised file {revised_file_path}: {e}"
+            )
+
+            # Fallback to simple revision summary
+            return self._create_fallback_revision_summary(
+                revised_file_path, revision_type
+            )
+
+    async def synchronize_multiple_revised_files(
+        self, client, client_type: str, revision_results: List[Dict[str, Any]]
+    ) -> Dict[str, str]:
+        """
+        Synchronize memory for multiple revised files based on revision results
+
+        Args:
+            client: LLM client instance
+            client_type: Type of LLM client
+            revision_results: List of revision results with file paths, diffs, and new content
+
+        Returns:
+            Dictionary mapping file paths to updated memory summaries
+        """
+        try:
+            self.logger.info(
+                f"🔄 Synchronizing memory for {len(revision_results)} revised files"
+            )
+
+            synchronized_summaries = {}
+
+            for revision_result in revision_results:
+                file_path = revision_result.get("file_path", "")
+                diff_content = revision_result.get("diff", "")
+                new_content = revision_result.get("new_content", "")
+                revision_type = revision_result.get("revision_type", "targeted_fix")
+
+                if file_path and revision_result.get("success", False):
+                    summary = await self.synchronize_revised_file_memory(
+                        client,
+                        client_type,
+                        file_path,
+                        diff_content,
+                        new_content,
+                        revision_type,
+                    )
+                    synchronized_summaries[file_path] = summary
+                else:
+                    self.logger.warning(
+                        f"⚠️ Skipping memory sync for failed revision: {file_path}"
+                    )
+
+            self.logger.info(
+                f"✅ Memory synchronized for {len(synchronized_summaries)} successfully revised files"
+            )
+
+            return synchronized_summaries
+
+        except Exception as e:
+            self.logger.error(
+                f"Failed to synchronize memory for multiple revised files: {e}"
+            )
+            return {}
+
+    def _create_file_revision_summary_prompt(
+        self, file_path: str, diff_content: str, new_content: str, revision_type: str
+    ) -> str:
+        """
+        Create prompt for LLM to generate file revision summary
+
+        Args:
+            file_path: Path of the revised file
+            diff_content: Unified diff showing changes
+            new_content: Complete new content of the file
+            revision_type: Type of revision performed
+
+        Returns:
+            Prompt for LLM revision summarization
+        """
+        # Truncate content if too long for prompt
+        content_preview = (
+            new_content[:2000] + "..." if len(new_content) > 2000 else new_content
+        )
+        diff_preview = (
+            diff_content[:1000] + "..." if len(diff_content) > 1000 else diff_content
+        )
+
+        prompt = f"""You are an expert code revision summarizer. A file has been REVISED with targeted changes. Create a structured summary of the revision.
+
+**File Revised**: {file_path}
+**Revision Type**: {revision_type}
+
+**Changes Made (Diff):**
+```diff
+{diff_preview}
+```
+
+**Updated File Content:**
+```python
+{content_preview}
+```
+
+**Required Summary Format:**
+
+**Revision Summary**:
+- Brief description of what was changed and why
+
+**Changes Made**:
+- Specific modifications applied (line-level changes)
+- Functions/classes affected
+- New functionality added or bugs fixed
+
+**Impact Assessment**:
+- How the changes affect the file's behavior
+- Dependencies that might be affected
+- Integration points that need attention
+
+**Quality Improvements**:
+- Code quality enhancements made
+- Error handling improvements
+- Performance or maintainability gains
+
+**Post-Revision Status**:
+- Current functionality of the file
+- Key interfaces and exports
+- Dependencies and imports
+
+**Instructions:**
+- Focus on the CHANGES made, not just the final state
+- Highlight the specific improvements and fixes applied
+- Be concise but comprehensive about the revision impact
+- Use the exact format specified above
+
+**Summary:**"""
+
+        return prompt
+
+    def _extract_revision_summary_sections(self, llm_summary: str) -> Dict[str, str]:
+        """
+        Extract different sections from LLM-generated revision summary
+
+        Args:
+            llm_summary: Raw LLM response containing revision summary
+
+        Returns:
+            Dictionary with extracted sections
+        """
+        sections = {
+            "revision_summary": "",
+            "changes_made": "",
+            "impact_assessment": "",
+            "quality_improvements": "",
+            "post_revision_status": "",
+        }
+
+        try:
+            lines = llm_summary.split("\n")
+            current_section = None
+            current_content = []
+
+            for line in lines:
+                line_lower = line.lower().strip()
+                original_line = line.strip()
+
+                # Skip empty lines
+                if not original_line:
+                    if current_section:
+                        current_content.append(line)
+                    continue
+
+                # Section detection
+                section_matched = False
+
+                if "revision summary" in line_lower and "**" in original_line:
+                    if current_section and current_content:
+                        sections[current_section] = "\n".join(current_content).strip()
+                    current_section = "revision_summary"
+                    current_content = []
+                    section_matched = True
+                elif "changes made" in line_lower and "**" in original_line:
+                    if current_section and current_content:
+                        sections[current_section] = "\n".join(current_content).strip()
+                    current_section = "changes_made"
+                    current_content = []
+                    section_matched = True
+                elif "impact assessment" in line_lower and "**" in original_line:
+                    if current_section and current_content:
+                        sections[current_section] = "\n".join(current_content).strip()
+                    current_section = "impact_assessment"
+                    current_content = []
+                    section_matched = True
+                elif "quality improvements" in line_lower and "**" in original_line:
+                    if current_section and current_content:
+                        sections[current_section] = "\n".join(current_content).strip()
+                    current_section = "quality_improvements"
+                    current_content = []
+                    section_matched = True
+                elif "post-revision status" in line_lower and "**" in original_line:
+                    if current_section and current_content:
+                        sections[current_section] = "\n".join(current_content).strip()
+                    current_section = "post_revision_status"
+                    current_content = []
+                    section_matched = True
+
+                # If no section header matched, add to current content
+                if not section_matched and current_section:
+                    current_content.append(line)
+
+            # Save the final section
+            if current_section and current_content:
+                sections[current_section] = "\n".join(current_content).strip()
+
+            self.logger.info(
+                f"📋 Extracted {len([s for s in sections.values() if s])} revision summary sections"
+            )
+
+        except Exception as e:
+            self.logger.error(f"Failed to extract revision summary sections: {e}")
+            # Provide fallback content
+            sections["revision_summary"] = "File revision completed"
+            sections["changes_made"] = (
+                "Targeted changes applied based on error analysis"
+            )
+            sections["impact_assessment"] = (
+                "Changes should improve code functionality and reduce errors"
+            )
+            sections["quality_improvements"] = (
+                "Code quality enhanced through targeted fixes"
+            )
+            sections["post_revision_status"] = "File functionality updated and improved"
+
+        return sections
+
+    def _format_file_revision_summary(
+        self,
+        file_path: str,
+        revision_sections: Dict[str, str],
+        diff_content: str,
+        revision_type: str,
+    ) -> str:
+        """
+        Format the revision summary into the final structure
+
+        Args:
+            file_path: Path of the revised file
+            revision_sections: Extracted sections from LLM summary
+            diff_content: Unified diff content
+            revision_type: Type of revision performed
+
+        Returns:
+            Formatted revision summary
+        """
+        timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+
+        # Format sections with fallbacks
+        revision_summary = revision_sections.get(
+            "revision_summary", "File revision completed"
+        )
+        changes_made = revision_sections.get("changes_made", "Targeted changes applied")
+        impact_assessment = revision_sections.get(
+            "impact_assessment", "Changes should improve functionality"
+        )
+        quality_improvements = revision_sections.get(
+            "quality_improvements", "Code quality enhanced"
+        )
+        post_revision_status = revision_sections.get(
+            "post_revision_status", "File updated successfully"
+        )
+
+        formatted_summary = f"""# File Revision Summary (UPDATED)
+**Generated**: {timestamp}
+**File Revised**: {file_path}
+**Revision Type**: {revision_type}
+
+## Revision Summary
+{revision_summary}
+
+## Changes Made
+{changes_made}
+
+## Impact Assessment
+{impact_assessment}
+
+## Quality Improvements
+{quality_improvements}
+
+## Post-Revision Status
+{post_revision_status}
+
+## Technical Details
+**Diff Applied:**
+```diff
+{diff_content[:500]}{"..." if len(diff_content) > 500 else ""}
+```
+
+---
+*Auto-generated by Enhanced Memory Agent (Revision Mode)*
+"""
+        return formatted_summary
+
+    def _create_fallback_revision_summary(
+        self, file_path: str, revision_type: str
+    ) -> str:
+        """
+        Create fallback revision summary when LLM is unavailable
+
+        Args:
+            file_path: Path of the revised file
+            revision_type: Type of revision performed
+
+        Returns:
+            Fallback revision summary
+        """
+        timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+
+        fallback_summary = f"""# File Revision Summary (UPDATED)
+**Generated**: {timestamp}
+**File Revised**: {file_path}
+**Revision Type**: {revision_type}
+
+## Revision Summary
+File has been revised with targeted changes. LLM summary generation failed.
+
+## Changes Made
+- Targeted modifications applied based on error analysis
+- Specific line-level changes implemented
+- Code functionality updated
+
+## Impact Assessment
+- File behavior should be improved
+- Error conditions addressed
+- Integration points maintained
+
+## Quality Improvements
+- Code quality enhanced through precise fixes
+- Error handling improved
+- Maintainability increased
+
+## Post-Revision Status
+- File successfully updated
+- Functionality preserved and enhanced
+- Ready for integration testing
+
+---
+*Auto-generated by Enhanced Memory Agent (Revision Fallback Mode)*
+"""
+        return fallback_summary
+
+    async def _save_revised_file_summary(self, revision_summary: str, file_path: str):
+        """
+        Save or update the revision summary for a file (replaces old summary)
+
+        Args:
+            revision_summary: New revision summary content
+            file_path: Path of the file for which the summary was generated
+        """
+        try:
+            # For revised files, we replace the existing summary rather than append
+            # Read existing content to find and replace the specific file's summary
+            file_exists = os.path.exists(self.code_summary_path)
+
+            if file_exists:
+                with open(self.code_summary_path, "r", encoding="utf-8") as f:
+                    existing_content = f.read()
+
+                # Look for existing summary for this file and replace it
+                import re
+
+                # Pattern to match existing implementation section for this file
+                file_pattern = re.escape(file_path)
+                section_pattern = rf"={80}\s*\n## IMPLEMENTATION File {file_pattern}\n={80}.*?(?=\n={80}|\Z)"
+
+                # Check if this file already has a summary
+                if re.search(section_pattern, existing_content, re.DOTALL):
+                    # Replace existing summary
+                    new_section = f"\n{'=' * 80}\n## IMPLEMENTATION File {file_path} (REVISED)\n{'=' * 80}\n\n{revision_summary}\n\n"
+                    updated_content = re.sub(
+                        section_pattern,
+                        new_section.strip(),
+                        existing_content,
+                        flags=re.DOTALL,
+                    )
+
+                    with open(self.code_summary_path, "w", encoding="utf-8") as f:
+                        f.write(updated_content)
+
+                    self.logger.info(
+                        f"Updated existing summary for revised file: {file_path}"
+                    )
+                else:
+                    # Append new summary for this file
+                    with open(self.code_summary_path, "a", encoding="utf-8") as f:
+                        f.write("\n" + "=" * 80 + "\n")
+                        f.write(f"## IMPLEMENTATION File {file_path} (REVISED)\n")
+                        f.write("=" * 80 + "\n\n")
+                        f.write(revision_summary)
+                        f.write("\n\n")
+
+                    self.logger.info(
+                        f"Appended new summary for revised file: {file_path}"
+                    )
+            else:
+                # Create new file with header
+                os.makedirs(os.path.dirname(self.code_summary_path), exist_ok=True)
+
+                with open(self.code_summary_path, "w", encoding="utf-8") as f:
+                    f.write("# Code Implementation Progress Summary\n")
+                    f.write("*Accumulated implementation progress for all files*\n\n")
+                    f.write("\n" + "=" * 80 + "\n")
+                    f.write(f"## IMPLEMENTATION File {file_path} (REVISED)\n")
+                    f.write("=" * 80 + "\n\n")
+                    f.write(revision_summary)
+                    f.write("\n\n")
+
+                self.logger.info(
+                    f"Created new summary file with revised file: {file_path}"
+                )
+
+        except Exception as e:
+            self.logger.error(
+                f"Failed to save revised file summary for {file_path}: {e}"
+            )
+
+    def get_revision_memory_statistics(
+        self, revised_files: List[str]
+    ) -> Dict[str, Any]:
+        """
+        Get memory statistics for revised files
+
+        Args:
+            revised_files: List of file paths that have been revised
+
+        Returns:
+            Dictionary with revision memory statistics
+        """
+        try:
+            total_revisions = len(revised_files)
+
+            # Count how many files have updated summaries
+            summaries_updated = 0
+            if os.path.exists(self.code_summary_path):
+                with open(self.code_summary_path, "r", encoding="utf-8") as f:
+                    content = f.read()
+
+                for file_path in revised_files:
+                    if f"File {file_path} (REVISED)" in content:
+                        summaries_updated += 1
+
+            return {
+                "total_revised_files": total_revisions,
+                "summaries_updated": summaries_updated,
+                "memory_sync_rate": (summaries_updated / total_revisions * 100)
+                if total_revisions > 0
+                else 0,
+                "revised_files_list": revised_files.copy(),
+                "memory_summary_path": self.code_summary_path,
+                "revision_memory_mode": "active",
+            }
+
+        except Exception as e:
+            self.logger.error(f"Failed to get revision memory statistics: {e}")
+            return {
+                "total_revised_files": len(revised_files),
+                "summaries_updated": 0,
+                "memory_sync_rate": 0,
+                "revised_files_list": revised_files.copy(),
+                "memory_summary_path": self.code_summary_path,
+                "revision_memory_mode": "error",
+            }

projects/ui/DeepCode/workflows/codebase_index_workflow.py

@@ -0,0 +1,732 @@
+"""
+Codebase Index Workflow
+
+This workflow integrates the functionality of run_indexer.py and code_indexer.py
+to build intelligent relationships between existing codebase and target structure.
+
+Features:
+- Extract target file structure from initial_plan.txt
+- Analyze codebase and build indexes
+- Generate relationship mappings and statistical reports
+- Provide reference basis for code reproduction
+"""
+
+import asyncio
+import json
+import logging
+import os
+import re
+import sys
+from pathlib import Path
+from typing import Dict, Any, Optional
+import yaml
+
+# Add tools directory to path
+sys.path.append(str(Path(__file__).parent.parent / "tools"))
+
+from tools.code_indexer import CodeIndexer
+
+
+class CodebaseIndexWorkflow:
+    """Codebase Index Workflow Class"""
+
+    def __init__(self, logger=None):
+        """
+        Initialize workflow
+
+        Args:
+            logger: Logger instance
+        """
+        self.logger = logger or self._setup_default_logger()
+        self.indexer = None
+
+    def _setup_default_logger(self) -> logging.Logger:
+        """Setup default logger"""
+        logger = logging.getLogger("CodebaseIndexWorkflow")
+        logger.setLevel(logging.INFO)
+
+        if not logger.handlers:
+            handler = logging.StreamHandler()
+            formatter = logging.Formatter(
+                "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
+            )
+            handler.setFormatter(formatter)
+            logger.addHandler(handler)
+
+        return logger
+
+    def extract_file_tree_from_plan(self, plan_content: str) -> Optional[str]:
+        """
+        Extract file tree structure from initial_plan.txt content
+
+        Args:
+            plan_content: Content of the initial_plan.txt file
+
+        Returns:
+            Extracted file tree structure as string
+        """
+        # Look for file structure section, specifically "## File Structure" format
+        file_structure_pattern = r"## File Structure[^\n]*\n```[^\n]*\n(.*?)\n```"
+
+        match = re.search(file_structure_pattern, plan_content, re.DOTALL)
+        if match:
+            file_tree = match.group(1).strip()
+            lines = file_tree.split("\n")
+
+            # Clean tree structure - remove empty lines and comments not part of structure
+            cleaned_lines = []
+            for line in lines:
+                # Keep tree structure lines
+                if line.strip() and (
+                    any(char in line for char in ["├──", "└──", "│"])
+                    or line.strip().endswith("/")
+                    or "." in line.split("/")[-1]  # has file extension
+                    or line.strip().endswith(".py")
+                    or line.strip().endswith(".txt")
+                    or line.strip().endswith(".md")
+                    or line.strip().endswith(".yaml")
+                ):
+                    cleaned_lines.append(line)
+
+            if len(cleaned_lines) >= 5:
+                file_tree = "\n".join(cleaned_lines)
+                self.logger.info(
+                    f"📊 Extracted file tree structure from ## File Structure section ({len(cleaned_lines)} lines)"
+                )
+                return file_tree
+
+        # Fallback: look for any code block containing project structure
+        code_block_patterns = [
+            r"```[^\n]*\n(project/.*?(?:├──|└──).*?)\n```",
+            r"```[^\n]*\n(src/.*?(?:├──|└──).*?)\n```",
+            r"```[^\n]*\n(core/.*?(?:├──|└──).*?)\n```",
+            r"```[^\n]*\n(.*?(?:├──|└──).*?(?:\.py|\.txt|\.md|\.yaml).*?)\n```",
+        ]
+
+        for pattern in code_block_patterns:
+            match = re.search(pattern, plan_content, re.DOTALL)
+            if match:
+                file_tree = match.group(1).strip()
+                lines = [line for line in file_tree.split("\n") if line.strip()]
+                if len(lines) >= 5:
+                    self.logger.info(
+                        f"📊 Extracted file tree structure from code block ({len(lines)} lines)"
+                    )
+                    return file_tree
+
+        # Final fallback: extract file paths from file mentions and create basic structure
+        self.logger.warning(
+            "⚠️ No standard file tree found, trying to extract from file mentions..."
+        )
+
+        # Search for file paths in backticks throughout the document
+        file_mentions = re.findall(
+            r"`([^`]*(?:\.py|\.txt|\.md|\.yaml|\.yml)[^`]*)`", plan_content
+        )
+
+        if file_mentions:
+            # Organize files into directory structure
+            dirs = set()
+            files_by_dir = {}
+
+            for file_path in file_mentions:
+                file_path = file_path.strip()
+                if "/" in file_path:
+                    dir_path = "/".join(file_path.split("/")[:-1])
+                    filename = file_path.split("/")[-1]
+                    dirs.add(dir_path)
+                    if dir_path not in files_by_dir:
+                        files_by_dir[dir_path] = []
+                    files_by_dir[dir_path].append(filename)
+                else:
+                    if "root" not in files_by_dir:
+                        files_by_dir["root"] = []
+                    files_by_dir["root"].append(file_path)
+
+            # Create tree structure
+            structure_lines = []
+
+            # Determine root directory name from common patterns
+            if any("src/" in f for f in file_mentions):
+                root_name = "src"
+            elif any("core/" in f for f in file_mentions):
+                root_name = "core"
+            elif any("lib/" in f for f in file_mentions):
+                root_name = "lib"
+            else:
+                root_name = "project"
+            structure_lines.append(f"{root_name}/")
+
+            # Add directories and files
+            sorted_dirs = sorted(dirs) if dirs else []
+            for i, dir_path in enumerate(sorted_dirs):
+                is_last_dir = i == len(sorted_dirs) - 1
+                prefix = "└──" if is_last_dir else "├──"
+                structure_lines.append(f"{prefix} {dir_path}/")
+
+                if dir_path in files_by_dir:
+                    files = sorted(files_by_dir[dir_path])
+                    for j, filename in enumerate(files):
+                        is_last_file = j == len(files) - 1
+                        if is_last_dir:
+                            file_prefix = "    └──" if is_last_file else "    ├──"
+                        else:
+                            file_prefix = "│   └──" if is_last_file else "│   ├──"
+                        structure_lines.append(f"{file_prefix} {filename}")
+
+            # Add root files (if any)
+            if "root" in files_by_dir:
+                root_files = sorted(files_by_dir["root"])
+                for i, filename in enumerate(root_files):
+                    is_last = (i == len(root_files) - 1) and not sorted_dirs
+                    prefix = "└──" if is_last else "├──"
+                    structure_lines.append(f"{prefix} {filename}")
+
+            if len(structure_lines) >= 3:
+                file_tree = "\n".join(structure_lines)
+                self.logger.info(
+                    f"📊 Generated file tree from file mentions ({len(structure_lines)} lines)"
+                )
+                return file_tree
+
+        # If no file tree found, return None
+        self.logger.warning("⚠️ No file tree structure found in initial plan")
+        return None
+
+    def load_target_structure_from_plan(self, plan_path: str) -> str:
+        """
+        Load target structure from initial_plan.txt and extract file tree
+
+        Args:
+            plan_path: Path to initial_plan.txt file
+
+        Returns:
+            Extracted file tree structure
+        """
+        try:
+            # Load complete plan content
+            with open(plan_path, "r", encoding="utf-8") as f:
+                plan_content = f.read()
+
+            self.logger.info(f"📄 Loaded initial plan ({len(plan_content)} characters)")
+
+            # Extract file tree structure
+            file_tree = self.extract_file_tree_from_plan(plan_content)
+
+            if file_tree:
+                self.logger.info(
+                    "✅ Successfully extracted file tree from initial plan"
+                )
+                self.logger.info("📋 Extracted structure preview:")
+                # Show first few lines of extracted tree
+                preview_lines = file_tree.split("\n")[:8]
+                for line in preview_lines:
+                    self.logger.info(f"   {line}")
+                if len(file_tree.split("\n")) > 8:
+                    self.logger.info(
+                        f"   ... {len(file_tree.split('\n')) - 8} more lines"
+                    )
+                return file_tree
+            else:
+                self.logger.warning("⚠️ Unable to extract file tree from initial plan")
+                self.logger.info("🔄 Falling back to default target structure")
+                return self.get_default_target_structure()
+
+        except Exception as e:
+            self.logger.error(f"❌ Failed to load initial plan file {plan_path}: {e}")
+            self.logger.info("🔄 Falling back to default target structure")
+            return self.get_default_target_structure()
+
+    def get_default_target_structure(self) -> str:
+        """Get default target structure"""
+        return """
+project/
+├── src/
+│   ├── core/
+│   │   ├── gcn.py        # GCN encoder
+│   │   ├── diffusion.py  # forward/reverse processes
+│   │   ├── denoiser.py   # denoising MLP
+│   │   └── fusion.py     # fusion combiner
+│   ├── models/           # model wrapper classes
+│   │   └── recdiff.py
+│   ├── utils/
+│   │   ├── data.py       # loading & preprocessing
+│   │   ├── predictor.py  # scoring functions
+��   │   ├── loss.py       # loss functions
+│   │   ├── metrics.py    # NDCG, Recall etc.
+│   │   └── sched.py      # beta/alpha schedule utils
+│   └── configs/
+│       └── default.yaml  # hyperparameters, paths
+├── tests/
+│   ├── test_gcn.py
+│   ├── test_diffusion.py
+│   ├── test_denoiser.py
+│   ├── test_loss.py
+│   └── test_pipeline.py
+├── docs/
+│   ├── architecture.md
+│   ├── api_reference.md
+│   └── README.md
+├── experiments/
+│   ├── run_experiment.py
+│   └── notebooks/
+│       └── analysis.ipynb
+├── requirements.txt
+└── setup.py
+"""
+
+    def load_or_create_indexer_config(self, paper_dir: str) -> Dict[str, Any]:
+        """
+        Load or create indexer configuration
+
+        Args:
+            paper_dir: Paper directory path
+
+        Returns:
+            Configuration dictionary
+        """
+        # Try to load existing configuration file
+        config_path = Path(__file__).parent.parent / "tools" / "indexer_config.yaml"
+
+        try:
+            if config_path.exists():
+                with open(config_path, "r", encoding="utf-8") as f:
+                    config = yaml.safe_load(f)
+
+                # Update path configuration to current paper directory
+                if "paths" not in config:
+                    config["paths"] = {}
+                config["paths"]["code_base_path"] = os.path.join(paper_dir, "code_base")
+                config["paths"]["output_dir"] = os.path.join(paper_dir, "indexes")
+
+                # Adjust performance settings for workflow
+                if "performance" in config:
+                    config["performance"]["enable_concurrent_analysis"] = (
+                        False  # Disable concurrency to avoid API limits
+                    )
+                if "debug" in config:
+                    config["debug"]["verbose_output"] = True  # Enable verbose output
+                if "llm" in config:
+                    config["llm"]["request_delay"] = 0.5  # Increase request delay
+
+                self.logger.info(f"Loaded configuration file: {config_path}")
+                return config
+
+        except Exception as e:
+            self.logger.warning(f"Failed to load configuration file: {e}")
+
+        # If loading fails, use default configuration
+        self.logger.info("Using default configuration")
+        default_config = {
+            "paths": {
+                "code_base_path": os.path.join(paper_dir, "code_base"),
+                "output_dir": os.path.join(paper_dir, "indexes"),
+            },
+            "llm": {
+                "model_provider": "anthropic",
+                "max_tokens": 4000,
+                "temperature": 0.3,
+                "request_delay": 0.5,  # Increase request delay
+                "max_retries": 3,
+                "retry_delay": 1.0,
+            },
+            "file_analysis": {
+                "max_file_size": 1048576,  # 1MB
+                "max_content_length": 3000,
+                "supported_extensions": [
+                    ".py",
+                    ".js",
+                    ".ts",
+                    ".java",
+                    ".cpp",
+                    ".c",
+                    ".h",
+                    ".hpp",
+                    ".cs",
+                    ".php",
+                    ".rb",
+                    ".go",
+                    ".rs",
+                    ".scala",
+                    ".kt",
+                    ".yaml",
+                    ".yml",
+                    ".json",
+                    ".xml",
+                    ".toml",
+                    ".md",
+                    ".txt",
+                ],
+                "skip_directories": [
+                    "__pycache__",
+                    "node_modules",
+                    "target",
+                    "build",
+                    "dist",
+                    "venv",
+                    "env",
+                    ".git",
+                    ".svn",
+                    "data",
+                    "datasets",
+                ],
+            },
+            "relationships": {
+                "min_confidence_score": 0.3,
+                "high_confidence_threshold": 0.7,
+                "relationship_types": {
+                    "direct_match": 1.0,
+                    "partial_match": 0.8,
+                    "reference": 0.6,
+                    "utility": 0.4,
+                },
+            },
+            "performance": {
+                "enable_concurrent_analysis": False,  # Disable concurrency to avoid API limits
+                "max_concurrent_files": 3,
+                "enable_content_caching": True,
+                "max_cache_size": 100,
+            },
+            "debug": {
+                "verbose_output": True,
+                "save_raw_responses": False,
+                "mock_llm_responses": False,
+            },
+            "output": {
+                "generate_summary": True,
+                "generate_statistics": True,
+                "include_metadata": True,
+                "json_indent": 2,
+            },
+            "logging": {"level": "INFO", "log_to_file": False},
+        }
+
+        return default_config
+
+    async def run_indexing_workflow(
+        self,
+        paper_dir: str,
+        initial_plan_path: Optional[str] = None,
+        config_path: str = "mcp_agent.secrets.yaml",
+    ) -> Dict[str, Any]:
+        """
+        Run the complete code indexing workflow
+
+        Args:
+            paper_dir: Paper directory path
+            initial_plan_path: Initial plan file path (optional)
+            config_path: API configuration file path
+
+        Returns:
+            Index result dictionary
+        """
+        try:
+            self.logger.info("🚀 Starting codebase index workflow...")
+
+            # Step 1: Determine initial plan file path
+            if not initial_plan_path:
+                initial_plan_path = os.path.join(paper_dir, "initial_plan.txt")
+
+            # Step 2: Load target structure
+            if os.path.exists(initial_plan_path):
+                self.logger.info(
+                    f"📐 Loading target structure from {initial_plan_path}"
+                )
+                target_structure = self.load_target_structure_from_plan(
+                    initial_plan_path
+                )
+            else:
+                self.logger.warning(
+                    f"⚠️ Initial plan file does not exist: {initial_plan_path}"
+                )
+                self.logger.info("📐 Using default target structure")
+                target_structure = self.get_default_target_structure()
+
+            # Step 3: Check codebase path
+            code_base_path = os.path.join(paper_dir, "code_base")
+            if not os.path.exists(code_base_path):
+                self.logger.error(f"❌ Codebase path does not exist: {code_base_path}")
+                return {
+                    "status": "error",
+                    "message": f"Code base path does not exist: {code_base_path}",
+                    "output_files": {},
+                }
+
+            # Step 4: Create output directory
+            output_dir = os.path.join(paper_dir, "indexes")
+            os.makedirs(output_dir, exist_ok=True)
+
+            # Step 5: Load configuration
+            indexer_config = self.load_or_create_indexer_config(paper_dir)
+
+            self.logger.info(f"📁 Codebase path: {code_base_path}")
+            self.logger.info(f"📤 Output directory: {output_dir}")
+
+            # Step 6: Create code indexer
+            self.indexer = CodeIndexer(
+                code_base_path=code_base_path,
+                target_structure=target_structure,
+                output_dir=output_dir,
+                config_path=config_path,
+                enable_pre_filtering=True,
+            )
+
+            # Apply configuration settings
+            self.indexer.indexer_config = indexer_config
+
+            # Directly set configuration attributes to indexer
+            if "file_analysis" in indexer_config:
+                file_config = indexer_config["file_analysis"]
+                self.indexer.supported_extensions = set(
+                    file_config.get(
+                        "supported_extensions", self.indexer.supported_extensions
+                    )
+                )
+                self.indexer.skip_directories = set(
+                    file_config.get("skip_directories", self.indexer.skip_directories)
+                )
+                self.indexer.max_file_size = file_config.get(
+                    "max_file_size", self.indexer.max_file_size
+                )
+                self.indexer.max_content_length = file_config.get(
+                    "max_content_length", self.indexer.max_content_length
+                )
+
+            if "llm" in indexer_config:
+                llm_config = indexer_config["llm"]
+                self.indexer.model_provider = llm_config.get(
+                    "model_provider", self.indexer.model_provider
+                )
+                self.indexer.llm_max_tokens = llm_config.get(
+                    "max_tokens", self.indexer.llm_max_tokens
+                )
+                self.indexer.llm_temperature = llm_config.get(
+                    "temperature", self.indexer.llm_temperature
+                )
+                self.indexer.request_delay = llm_config.get(
+                    "request_delay", self.indexer.request_delay
+                )
+                self.indexer.max_retries = llm_config.get(
+                    "max_retries", self.indexer.max_retries
+                )
+                self.indexer.retry_delay = llm_config.get(
+                    "retry_delay", self.indexer.retry_delay
+                )
+
+            if "relationships" in indexer_config:
+                rel_config = indexer_config["relationships"]
+                self.indexer.min_confidence_score = rel_config.get(
+                    "min_confidence_score", self.indexer.min_confidence_score
+                )
+                self.indexer.high_confidence_threshold = rel_config.get(
+                    "high_confidence_threshold", self.indexer.high_confidence_threshold
+                )
+                self.indexer.relationship_types = rel_config.get(
+                    "relationship_types", self.indexer.relationship_types
+                )
+
+            if "performance" in indexer_config:
+                perf_config = indexer_config["performance"]
+                self.indexer.enable_concurrent_analysis = perf_config.get(
+                    "enable_concurrent_analysis",
+                    self.indexer.enable_concurrent_analysis,
+                )
+                self.indexer.max_concurrent_files = perf_config.get(
+                    "max_concurrent_files", self.indexer.max_concurrent_files
+                )
+                self.indexer.enable_content_caching = perf_config.get(
+                    "enable_content_caching", self.indexer.enable_content_caching
+                )
+                self.indexer.max_cache_size = perf_config.get(
+                    "max_cache_size", self.indexer.max_cache_size
+                )
+
+            if "debug" in indexer_config:
+                debug_config = indexer_config["debug"]
+                self.indexer.verbose_output = debug_config.get(
+                    "verbose_output", self.indexer.verbose_output
+                )
+                self.indexer.save_raw_responses = debug_config.get(
+                    "save_raw_responses", self.indexer.save_raw_responses
+                )
+                self.indexer.mock_llm_responses = debug_config.get(
+                    "mock_llm_responses", self.indexer.mock_llm_responses
+                )
+
+            if "output" in indexer_config:
+                output_config = indexer_config["output"]
+                self.indexer.generate_summary = output_config.get(
+                    "generate_summary", self.indexer.generate_summary
+                )
+                self.indexer.generate_statistics = output_config.get(
+                    "generate_statistics", self.indexer.generate_statistics
+                )
+                self.indexer.include_metadata = output_config.get(
+                    "include_metadata", self.indexer.include_metadata
+                )
+
+            self.logger.info("🔧 Indexer configuration completed")
+            self.logger.info(f"🤖 Model provider: {self.indexer.model_provider}")
+            self.logger.info(
+                f"⚡ Concurrent analysis: {'Enabled' if self.indexer.enable_concurrent_analysis else 'Disabled'}"
+            )
+            self.logger.info(
+                f"🗄️ Content caching: {'Enabled' if self.indexer.enable_content_caching else 'Disabled'}"
+            )
+            self.logger.info(
+                f"🔍 Pre-filtering: {'Enabled' if self.indexer.enable_pre_filtering else 'Disabled'}"
+            )
+
+            self.logger.info("=" * 60)
+            self.logger.info("🚀 Starting code indexing process...")
+
+            # Step 7: Build all indexes
+            output_files = await self.indexer.build_all_indexes()
+
+            # Step 8: Generate summary report
+            if output_files:
+                summary_report = self.indexer.generate_summary_report(output_files)
+
+                self.logger.info("=" * 60)
+                self.logger.info("✅ Indexing completed successfully!")
+                self.logger.info(f"📊 Processed {len(output_files)} repositories")
+                self.logger.info("📁 Generated index files:")
+                for repo_name, file_path in output_files.items():
+                    self.logger.info(f"   📄 {repo_name}: {file_path}")
+                self.logger.info(f"📋 Summary report: {summary_report}")
+
+                # Statistics (if enabled)
+                if self.indexer.generate_statistics:
+                    self.logger.info("\n📈 Processing statistics:")
+                    total_relationships = 0
+                    high_confidence_relationships = 0
+
+                    for file_path in output_files.values():
+                        try:
+                            with open(file_path, "r", encoding="utf-8") as f:
+                                index_data = json.load(f)
+                                relationships = index_data.get("relationships", [])
+                                total_relationships += len(relationships)
+                                high_confidence_relationships += len(
+                                    [
+                                        r
+                                        for r in relationships
+                                        if r.get("confidence_score", 0)
+                                        > self.indexer.high_confidence_threshold
+                                    ]
+                                )
+                        except Exception as e:
+                            self.logger.warning(
+                                f"   ⚠️ Unable to load statistics from {file_path}: {e}"
+                            )
+
+                    self.logger.info(
+                        f"   🔗 Total relationships found: {total_relationships}"
+                    )
+                    self.logger.info(
+                        f"   ⭐ High confidence relationships: {high_confidence_relationships}"
+                    )
+                    self.logger.info(
+                        f"   📊 Average relationships per repository: {total_relationships / len(output_files) if output_files else 0:.1f}"
+                    )
+
+                self.logger.info("\n🎉 Code indexing process completed successfully!")
+
+                return {
+                    "status": "success",
+                    "message": f"Successfully indexed {len(output_files)} repositories",
+                    "output_files": output_files,
+                    "summary_report": summary_report,
+                    "statistics": {
+                        "total_repositories": len(output_files),
+                        "total_relationships": total_relationships,
+                        "high_confidence_relationships": high_confidence_relationships,
+                    }
+                    if self.indexer.generate_statistics
+                    else None,
+                }
+            else:
+                self.logger.warning("⚠️ No index files generated")
+                return {
+                    "status": "warning",
+                    "message": "No index files were generated",
+                    "output_files": {},
+                }
+
+        except Exception as e:
+            self.logger.error(f"❌ Index workflow failed: {e}")
+            # If there are detailed error messages, log them
+            import traceback
+
+            self.logger.error(f"Detailed error information: {traceback.format_exc()}")
+            return {"status": "error", "message": str(e), "output_files": {}}
+
+    def print_banner(self):
+        """Print application banner"""
+        banner = """
+╔═══════════════════════════════════════════════════════════════════════╗
+║                    🔍 Codebase Index Workflow v1.0                   ║
+║              Intelligent Code Relationship Analysis Tool              ║
+╠═══════════════════════════════════════════════════════════════════════╣
+║  📁 Analyzes existing codebases                                      ║
+║  🔗 Builds intelligent relationships with target structure           ║
+║  🤖 Powered by LLM analysis                                          ║
+║  📊 Generates detailed JSON indexes                                   ║
+║  🎯 Provides reference for code reproduction                          ║
+╚═══════════════════════════════════════════════════════════════════════╝
+        """
+        print(banner)
+
+
+# Convenience function for direct workflow invocation
+async def run_codebase_indexing(
+    paper_dir: str,
+    initial_plan_path: Optional[str] = None,
+    config_path: str = "mcp_agent.secrets.yaml",
+    logger=None,
+) -> Dict[str, Any]:
+    """
+    Convenience function to run codebase indexing
+
+    Args:
+        paper_dir: Paper directory path
+        initial_plan_path: Initial plan file path (optional)
+        config_path: API configuration file path
+        logger: Logger instance (optional)
+
+    Returns:
+        Index result dictionary
+    """
+    workflow = CodebaseIndexWorkflow(logger=logger)
+    workflow.print_banner()
+
+    return await workflow.run_indexing_workflow(
+        paper_dir=paper_dir,
+        initial_plan_path=initial_plan_path,
+        config_path=config_path,
+    )
+
+
+# Main function for testing
+async def main():
+    """Main function for testing workflow"""
+    import logging
+
+    # Setup logging
+    logging.basicConfig(level=logging.INFO)
+    logger = logging.getLogger(__name__)
+
+    # Test parameters
+    paper_dir = "./deepcode_lab/papers/1"
+    initial_plan_path = os.path.join(paper_dir, "initial_plan.txt")
+
+    # Run workflow
+    result = await run_codebase_indexing(
+        paper_dir=paper_dir, initial_plan_path=initial_plan_path, logger=logger
+    )
+
+    logger.info(f"Index result: {result}")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

projects/ui/serena-new/.devcontainer/devcontainer.json

@@ -0,0 +1,16 @@
+{
+  "name": "serena Project",
+  "dockerFile": "../Dockerfile",
+  "workspaceFolder": "/workspaces/serena",
+  "settings": {
+    "terminal.integrated.shell.linux": "/bin/bash",
+    "python.pythonPath": "/usr/local/bin/python",
+  },
+  "extensions": [
+    "ms-python.python",
+    "ms-toolsai.jupyter",
+    "ms-python.vscode-pylance"
+  ],
+  "forwardPorts": [],
+  "remoteUser": "root",
+}

projects/ui/serena-new/.github/FUNDING.yml

@@ -0,0 +1,3 @@
+# These are supported funding model platforms
+
+github: oraios

projects/ui/serena-new/.serena/project.yml

@@ -0,0 +1,66 @@
+# language of the project (csharp, python, rust, java, typescript, javascript, go, cpp, or ruby)
+# Special requirements:
+#  * csharp: Requires the presence of a .sln file in the project folder.
+language: python
+
+# whether to use the project's gitignore file to ignore files
+# Added on 2025-04-07
+ignore_all_files_in_gitignore: true
+# list of additional paths to ignore
+# same syntax as gitignore, so you can use * and **
+# Was previously called `ignored_dirs`, please update your config if you are using that.
+# Added (renamed)on 2025-04-07
+ignored_paths: []
+
+# whether the project is in read-only mode
+# If set to true, all editing tools will be disabled and attempts to use them will result in an error
+# Added on 2025-04-18
+read_only: false
+
+
+# list of tool names to exclude. We recommend not excluding any tools, see the readme for more details.
+# Below is the complete list of tools for convenience.
+# To make sure you have the latest list of tools, and to view their descriptions, 
+# execute `uv run scripts/print_tool_overview.py`.
+#
+#  * `activate_project`: Activates a project by name.
+#  * `check_onboarding_performed`: Checks whether project onboarding was already performed.
+#  * `create_text_file`: Creates/overwrites a file in the project directory.
+#  * `delete_lines`: Deletes a range of lines within a file.
+#  * `delete_memory`: Deletes a memory from Serena's project-specific memory store.
+#  * `execute_shell_command`: Executes a shell command.
+#  * `find_referencing_code_snippets`: Finds code snippets in which the symbol at the given location is referenced.
+#  * `find_referencing_symbols`: Finds symbols that reference the symbol at the given location (optionally filtered by type).
+#  * `find_symbol`: Performs a global (or local) search for symbols with/containing a given name/substring (optionally filtered by type).
+#  * `get_current_config`: Prints the current configuration of the agent, including the active and available projects, tools, contexts, and modes.
+#  * `get_symbols_overview`: Gets an overview of the top-level symbols defined in a given file.
+#  * `initial_instructions`: Gets the initial instructions for the current project.
+#     Should only be used in settings where the system prompt cannot be set,
+#     e.g. in clients you have no control over, like Claude Desktop.
+#  * `insert_after_symbol`: Inserts content after the end of the definition of a given symbol.
+#  * `insert_at_line`: Inserts content at a given line in a file.
+#  * `insert_before_symbol`: Inserts content before the beginning of the definition of a given symbol.
+#  * `list_dir`: Lists files and directories in the given directory (optionally with recursion).
+#  * `list_memories`: Lists memories in Serena's project-specific memory store.
+#  * `onboarding`: Performs onboarding (identifying the project structure and essential tasks, e.g. for testing or building).
+#  * `prepare_for_new_conversation`: Provides instructions for preparing for a new conversation (in order to continue with the necessary context).
+#  * `read_file`: Reads a file within the project directory.
+#  * `read_memory`: Reads the memory with the given name from Serena's project-specific memory store.
+#  * `remove_project`: Removes a project from the Serena configuration.
+#  * `replace_lines`: Replaces a range of lines within a file with new content.
+#  * `replace_symbol_body`: Replaces the full definition of a symbol.
+#  * `restart_language_server`: Restarts the language server, may be necessary when edits not through Serena happen.
+#  * `search_for_pattern`: Performs a search for a pattern in the project.
+#  * `summarize_changes`: Provides instructions for summarizing the changes made to the codebase.
+#  * `switch_modes`: Activates modes by providing a list of their names
+#  * `think_about_collected_information`: Thinking tool for pondering the completeness of collected information.
+#  * `think_about_task_adherence`: Thinking tool for determining whether the agent is still on track with the current task.
+#  * `think_about_whether_you_are_done`: Thinking tool for determining whether the task is truly completed.
+#  * `write_memory`: Writes a named memory (for future reference) to Serena's project-specific memory store.
+excluded_tools: []
+
+# initial prompt for the project. It will always be given to the LLM upon activating the project
+# (contrary to the memories, which are loaded on demand).
+initial_prompt: ""
+
+project_name: "serena"

projects/ui/serena-new/.vscode/settings.json

@@ -0,0 +1,14 @@
+{
+    "cSpell.words": [
+        "agno",
+        "asyncio",
+        "genai",
+        "getpid",
+        "Gopls",
+        "langsrv",
+        "multilspy",
+        "pixi",
+        "sensai",
+        "vibing"
+    ],
+}

projects/ui/serena-new/docs/custom_agent.md

@@ -0,0 +1,67 @@
+# Custom Agents with Serena
+
+As a reference implementation, we provide an integration with the [Agno](https://docs.agno.com/introduction/playground) agent framework.
+Agno is a model-agnostic agent framework that allows you to turn Serena into an agent 
+(independent of the MCP technology) with a large number of underlying LLMs. While Agno has recently
+added support for MCP servers out of the box, our Agno integration predates this and is a good illustration of how
+easy it is to integrate Serena into an arbitrary agent framework.
+
+Here's how it works:
+
+1. Download the agent-ui code with npx
+   ```shell
+   npx create-agent-ui@latest
+   ```
+   or, alternatively, clone it manually:
+   ```shell
+   git clone https://github.com/agno-agi/agent-ui.git
+   cd agent-ui 
+   pnpm install 
+   pnpm dev
+   ```
+
+2. Install serena with the optional requirements:
+   ```shell
+   # You can also only select agno,google or agno,anthropic instead of all-extras
+   uv pip install --all-extras -r pyproject.toml -e .
+   ```
+   
+3. Copy `.env.example` to `.env` and fill in the API keys for the provider(s) you
+   intend to use.
+
+4. Start the agno agent app with
+   ```shell
+   uv run python scripts/agno_agent.py
+   ```
+   By default, the script uses Claude as the model, but you can choose any model
+   supported by Agno (which is essentially any existing model).
+
+5. In a new terminal, start the agno UI with
+   ```shell
+   cd agent-ui 
+   pnpm dev
+   ```
+   Connect the UI to the agent you started above and start chatting. You will have
+   the same tools as in the MCP server version.
+
+
+Here is a short demo of Serena performing a small analysis task with the newest Gemini model:
+
+https://github.com/user-attachments/assets/ccfcb968-277d-4ca9-af7f-b84578858c62
+
+
+⚠️ IMPORTANT: In contrast to the MCP server approach, tool execution in the Agno UI does
+not ask for the user's permission. The shell tool is particularly critical, as it can perform arbitrary code execution. 
+While we have never encountered any issues with
+this in our testing with Claude, allowing this may not be entirely safe. 
+You may choose to disable certain tools for your setup in your Serena project's
+configuration file (`.yml`).
+
+
+## Other Agent Frameworks
+
+It should be straightforward to incorporate Serena into any
+agent framework (like [pydantic-ai](https://ai.pydantic.dev/), [langgraph](https://langchain-ai.github.io/langgraph/tutorials/introduction/) or others).
+Typically, you need only to write an adapter for Serena's tools to the tool representation in the framework of your choice, 
+as was done by us for Agno with [SerenaAgnoToolkit](/src/serena/agno.py).
+