diff --git a/docs_reviewer/docs_reviewer/__init__.py b/docs_reviewer/docs_reviewer/__init__.py
new file mode 100644
index 00000000..4c6dbc21
--- /dev/null
+++ b/docs_reviewer/docs_reviewer/__init__.py
@@ -0,0 +1,3 @@
+"""Docs Reviewer CLI - A tool for reviewing and validating documentation code snippets."""
+
+__version__ = "0.1.0"
diff --git a/docs_reviewer/docs_reviewer/agent.py b/docs_reviewer/docs_reviewer/agent.py
new file mode 100644
index 00000000..446985e6
--- /dev/null
+++ b/docs_reviewer/docs_reviewer/agent.py
@@ -0,0 +1,216 @@
+"""Code execution via bash with uv/uvx for isolated environments."""
+
+import subprocess
+import tempfile
+from pathlib import Path
+from typing import Any, Optional
+from langchain_core.tools import tool
+
+
+def create_code_execution_tool():
+    """
+    Create a tool that executes code snippets using bash.
+
+    For Python: Uses uv/uvx to create isolated environments with dependencies
+    For JavaScript: Uses node directly
+    For Bash: Executes directly
+
+    This tool will be called multiple times in parallel by the main agent.
+    """
+
+    @tool
+    def execute_code_snippet(
+        code: str,
+        language: str,
+        dependencies: Optional[list[str]] = None,
+        python_version: Optional[str] = None
+    ) -> str:
+        """
+        Execute a code snippet in an isolated environment and validate it works.
+
+        This tool runs code locally using bash. For Python, it uses uv/uvx to create
+        isolated environments with specified dependencies.
+
+        Args:
+            code: The code to execute
+            language: Programming language (python, javascript, bash, etc.)
+            dependencies: List of package dependencies (e.g., ["pandas", "requests"])
+            python_version: Python version to use (e.g., "3.11", "3.12")
+
+        Returns:
+            JSON string with execution results: {"success": bool, "output": str, "error": str}
+        """
+        dependencies = dependencies or []
+
+        try:
+            if language.lower() in ('python', 'py'):
+                return _execute_python(code, dependencies, python_version)
+            elif language.lower() in ('javascript', 'js'):
+                return _execute_javascript(code, dependencies)
+            elif language.lower() in ('bash', 'sh'):
+                return _execute_bash(code)
+            else:
+                return f'{{"success": false, "output": "", "error": "Unsupported language: {language}"}}'
+
+        except Exception as e:
+            return f'{{"success": false, "output": "", "error": "Execution error: {str(e)}"}}'
+
+    def _execute_python(code: str, dependencies: list[str], python_version: Optional[str]) -> str:
+        """Execute Python code using uvx for isolated environment."""
+        with tempfile.TemporaryDirectory() as tmpdir:
+            tmp_path = Path(tmpdir)
+            script_file = tmp_path / "script.py"
+            script_file.write_text(code)
+
+            # Build uvx command
+            if dependencies:
+                # Use uvx to run with dependencies
+                deps_args = " ".join([f"--with {dep}" for dep in dependencies])
+                python_flag = f"--python {python_version}" if python_version else ""
+                cmd = f"uvx {python_flag} {deps_args} python {script_file}"
+            else:
+                # Just use uv run for simple execution
+                python_flag = f"--python {python_version}" if python_version else ""
+                cmd = f"uv run {python_flag} python {script_file}"
+
+            # Execute
+            result = subprocess.run(
+                cmd,
+                shell=True,
+                capture_output=True,
+                text=True,
+                timeout=30,
+                cwd=tmpdir
+            )
+
+            success = result.returncode == 0
+            output = result.stdout.strip()
+            error = result.stderr.strip()
+
+            # Format as JSON string
+            import json
+            return json.dumps({
+                "success": success,
+                "output": output,
+                "error": error
+            })
+
+    def _execute_javascript(code: str, dependencies: list[str]) -> str:
+        """Execute JavaScript code using node."""
+        with tempfile.TemporaryDirectory() as tmpdir:
+            tmp_path = Path(tmpdir)
+            script_file = tmp_path / "script.js"
+
+            # If dependencies, create package.json and install
+            if dependencies:
+                package_json = {
+                    "type": "module",
+                    "dependencies": {dep: "latest" for dep in dependencies}
+                }
+                import json
+                (tmp_path / "package.json").write_text(json.dumps(package_json, indent=2))
+
+                # Install dependencies
+                install_result = subprocess.run(
+                    "npm install",
+                    shell=True,
+                    capture_output=True,
+                    text=True,
+                    timeout=60,
+                    cwd=tmpdir
+                )
+
+                if install_result.returncode != 0:
+                    return json.dumps({
+                        "success": False,
+                        "output": "",
+                        "error": f"Failed to install dependencies: {install_result.stderr}"
+                    })
+
+            script_file.write_text(code)
+
+            # Execute
+            result = subprocess.run(
+                f"node {script_file}",
+                shell=True,
+                capture_output=True,
+                text=True,
+                timeout=30,
+                cwd=tmpdir
+            )
+
+            success = result.returncode == 0
+            output = result.stdout.strip()
+            error = result.stderr.strip()
+
+            import json
+            return json.dumps({
+                "success": success,
+                "output": output,
+                "error": error
+            })
+
+    def _execute_bash(code: str) -> str:
+        """Execute bash code directly."""
+        result = subprocess.run(
+            code,
+            shell=True,
+            capture_output=True,
+            text=True,
+            timeout=30
+        )
+
+        success = result.returncode == 0
+        output = result.stdout.strip()
+        error = result.stderr.strip()
+
+        import json
+        return json.dumps({
+            "success": success,
+            "output": output,
+            "error": error
+        })
+
+    return execute_code_snippet
+
+
+class DocsReviewerAgent:
+    """Agent that reviews docs by calling execute_code_snippet tool in parallel."""
+
+    def __init__(self):
+        """Initialize the agent - no longer needed, kept for compatibility."""
+        pass
+
+    def review_snippets(
+        self,
+        markdown_file: Path,
+        snippets: list['CodeSnippet'],
+    ) -> list[dict[str, Any]]:
+        """
+        Review snippets by returning them for the CLI agent to process.
+
+        The CLI agent will call execute_code_snippet tool for each snippet,
+        and LangGraph will handle parallel execution automatically.
+
+        Args:
+            markdown_file: Path to markdown file
+            snippets: List of code snippets
+
+        Returns:
+            List of snippet metadata (execution happens via tool calls)
+        """
+        from docs_reviewer.markdown_parser import filter_executable_snippets
+
+        # Just return executable snippets - the main agent will execute them
+        executable = filter_executable_snippets(snippets)
+
+        return [
+            {
+                "snippet_index": i,
+                "code": s["code"],
+                "language": s["language"],
+                "start_line": s["start_line"],
+                "end_line": s["end_line"],
+            }
+            for i, s in enumerate(executable)
+        ]
diff --git a/docs_reviewer/docs_reviewer/cli.py b/docs_reviewer/docs_reviewer/cli.py
new file mode 100644
index 00000000..fb07296f
--- /dev/null
+++ b/docs_reviewer/docs_reviewer/cli.py
@@ -0,0 +1,180 @@
+"""Conversational CLI powered by DeepAgents."""
+
+import os
+from pathlib import Path
+from typing import Optional
+
+import typer
+from rich.console import Console
+from rich.prompt import Prompt
+from dotenv import load_dotenv
+
+from docs_reviewer.cli_agent import DocsReviewerCLIAgent
+
+# Load environment variables from .env file if it exists
+load_dotenv()
+
+app = typer.Typer(
+    name="docs-reviewer",
+    help="AI-powered documentation code reviewer",
+    add_completion=False,
+)
+console = Console()
+
+
+def check_api_key() -> bool:
+    """Check if Anthropic API key is configured."""
+    return bool(os.environ.get("ANTHROPIC_API_KEY"))
+
+
+def show_welcome():
+    """Display welcome message."""
+    console.print("\n[bold #beb4fd]Docs Reviewer[/bold #beb4fd] - AI-powered documentation code reviewer\n")
+    console.print("I can review markdown files and fix broken code snippets.")
+    console.print("Just provide a file path to get started!\n")
+
+
+def show_setup_instructions():
+    """Show setup instructions if not configured."""
+    console.print("\n[yellow]⚠️  API key required![/yellow]\n")
+    console.print("Set your Anthropic API key:")
+    console.print("   [cyan]export ANTHROPIC_API_KEY='your-api-key'[/cyan]\n")
+    console.print("Then run: [cyan]docs-reviewer chat[/cyan]\n")
+
+
+@app.command()
+def chat(
+    message: Optional[str] = typer.Option(
+        None,
+        "--message",
+        "-m",
+        help="Single message to process (non-interactive mode)",
+    ),
+    no_mcp: bool = typer.Option(
+        False,
+        "--no-mcp",
+        help="Disable MCP integration for LangChain docs",
+    ),
+) -> None:
+    """Start interactive chat session with the docs reviewer agent."""
+    import asyncio
+
+    # Check API key
+    if not check_api_key():
+        show_setup_instructions()
+        raise typer.Exit(1)
+
+    # Show minimal welcome message (only in interactive mode)
+    if not message:
+        show_welcome()
+        console.print()
+
+    # Run async initialization and chat loop
+    # Use asyncio.run to create a new event loop
+    try:
+        asyncio.run(_async_chat(message, enable_mcp=not no_mcp))
+    except RuntimeError as e:
+        if "asyncio.run() cannot be called from a running event loop" in str(e):
+            # Already in an async context (e.g., Jupyter)
+            # Get the current loop and run the coroutine
+            import sys
+            if sys.platform == 'win32':
+                asyncio.set_event_loop_policy(asyncio.WindowsProactorEventLoopPolicy())
+
+            loop = asyncio.get_event_loop()
+            loop.run_until_complete(_async_chat(message, enable_mcp=not no_mcp))
+        else:
+            raise
+
+
+async def _async_chat(message: Optional[str], enable_mcp: bool = True) -> None:
+    """Async chat handler for proper MCP initialization."""
+    # Initialize the CLI agent (simple - just needs API key in env)
+    try:
+        agent = DocsReviewerCLIAgent(enable_mcp=enable_mcp)
+        await agent.async_init()
+    except Exception as e:
+        console.print(f"[red]Error initializing agent: {e}[/red]")
+        raise typer.Exit(1)
+
+    # Single message mode
+    if message:
+        console.print("\n[bold #beb4fd]Docs Reviewer[/bold #beb4fd]")
+        response = await agent.process_message(message, console)
+        console.print()
+        return
+
+    # Interactive chat loop with command history
+    conversation_active = True
+
+    # Use prompt_toolkit for better input with persistent history
+    # IMPORTANT: Use async version (prompt_async) when in async context
+    try:
+        from prompt_toolkit import PromptSession
+        from prompt_toolkit.history import FileHistory
+        from prompt_toolkit.styles import Style
+
+        # Store history in user's home directory
+        history_file = Path.home() / ".docs_reviewer_history"
+        prompt_history = FileHistory(str(history_file))
+        prompt_style = Style.from_dict({
+            'prompt': '#beb4fd bold',
+        })
+        session = PromptSession(history=prompt_history, style=prompt_style)
+        use_prompt_toolkit = True
+    except ImportError:
+        use_prompt_toolkit = False
+        console.print("[dim]Tip: Install prompt_toolkit for command history (pip install prompt_toolkit)[/dim]\n")
+
+    while conversation_active:
+        try:
+            # Get user input with history support
+            if use_prompt_toolkit:
+                from prompt_toolkit.formatted_text import HTML
+                # Use prompt_async() in async context, not prompt()!
+                user_input = await session.prompt_async(HTML('\n<prompt>You:</prompt> '))
+            else:
+                user_input = Prompt.ask("\n[bold #beb4fd]You[/bold #beb4fd]")
+
+            if user_input.lower() in ["exit", "quit", "bye", "goodbye"]:
+                console.print()
+                conversation_active = False
+                continue
+
+            if not user_input.strip():
+                continue
+
+            # Process with agent - show streaming progress
+            console.print("\n[bold #beb4fd]Docs Reviewer[/bold #beb4fd]")
+            response = await agent.process_message(user_input, console)
+            console.print()
+
+        except KeyboardInterrupt:
+            console.print("\n")
+            conversation_active = False
+        except Exception as e:
+            console.print(f"\n[red]Error: {e}[/red]")
+
+
+@app.callback(invoke_without_command=True)
+def main(
+    ctx: typer.Context,
+    version: bool = typer.Option(False, "--version", "-v", help="Show version and exit"),
+    message: Optional[str] = typer.Option(None, "--message", "-m", help="Single message mode"),
+) -> None:
+    """
+    Docs Reviewer - AI-powered documentation code reviewer.
+
+    Just run 'docs-reviewer' to start chatting!
+    """
+    if version:
+        console.print("docs-reviewer version 0.1.0")
+        raise typer.Exit()
+
+    # If no subcommand was invoked, start chat by default
+    if ctx.invoked_subcommand is None:
+        ctx.invoke(chat, message=message)
+
+
+if __name__ == "__main__":
+    app()
diff --git a/docs_reviewer/docs_reviewer/cli_agent.py b/docs_reviewer/docs_reviewer/cli_agent.py
new file mode 100644
index 00000000..b82d95e3
--- /dev/null
+++ b/docs_reviewer/docs_reviewer/cli_agent.py
@@ -0,0 +1,890 @@
+"""CLI agent that handles natural language commands for docs reviewing."""
+
+import os
+from pathlib import Path
+from typing import Any, Optional
+
+from langchain_anthropic import ChatAnthropic
+from langchain_core.tools import tool
+from langchain_core.messages import HumanMessage, AIMessage
+from typing import TypedDict, Annotated
+from langgraph.graph.message import add_messages
+from langchain_mcp_adapters.client import MultiServerMCPClient
+
+from deepagents import create_deep_agent
+from docs_reviewer.markdown_parser import extract_code_snippets, filter_executable_snippets, categorize_snippets
+from docs_reviewer.markdown_writer import write_corrected_markdown, write_review_report
+from docs_reviewer.agent import create_code_execution_tool
+
+
+async def _get_langchain_docs_tools() -> list:
+    """Get MCP tools for accessing LangChain documentation."""
+    servers = {
+        "fetch": {
+            "command": "uvx",
+            "args": ["mcp-server-fetch"],
+            "env": {},
+            "transport": "stdio",
+        }
+    }
+    client = MultiServerMCPClient(servers)
+    return await client.get_tools()
+
+
+class DocsReviewerState(TypedDict):
+    """State schema for the docs reviewer agent."""
+    messages: Annotated[list, add_messages]
+    file_snippets_cache: dict[str, dict]  # Maps file paths to snippet data
+
+
+class DocsReviewerCLIAgent:
+    """Simple agent for natural language docs reviewing."""
+
+    def __init__(self, enable_mcp: bool = True):
+        """
+        Initialize the CLI agent.
+
+        Args:
+            enable_mcp: Whether to enable MCP tools for LangChain docs access
+        """
+        import uuid
+
+        self.current_working_directory = Path.cwd()
+        self.thread_id = f"docs-reviewer-{uuid.uuid4().hex[:8]}"  # Unique thread per session
+        self.last_message_count = 0  # Track how many messages we've displayed
+        self.enable_mcp = enable_mcp
+        self.mcp_manager = None
+        self.agent = None  # Will be initialized in async_init
+
+        # Get API key
+        api_key = os.environ.get("ANTHROPIC_API_KEY")
+        if not api_key:
+            raise ValueError("ANTHROPIC_API_KEY not found in environment")
+
+        # Initialize LLM - simple, no config needed
+        self.llm = ChatAnthropic(
+            model="claude-sonnet-4-5-20250929",
+            temperature=0.1,
+            api_key=api_key,
+        )
+
+        # Create base tools
+        self.tools = self._create_cli_tools()
+
+    async def async_init(self) -> "DocsReviewerCLIAgent":
+        """
+        Async initialization for MCP tools.
+
+        This must be called before using the agent if MCP is enabled.
+
+        Returns:
+            Self for chaining
+        """
+        # Get MCP tools if enabled (for subagents only)
+        mcp_tools = []
+        if self.enable_mcp:
+            try:
+                mcp_tools = await _get_langchain_docs_tools()
+            except Exception as e:
+                # MCP is optional - continue without it if it fails
+                print(f"Warning: Could not initialize MCP tools: {e}")
+                print("Continuing without LangChain docs access...")
+
+        # Create code execution tool for subagents
+        execute_code_snippet = create_code_execution_tool()
+
+        # Build subagent tools list - includes both execution and MCP tools
+        subagent_tools = [execute_code_snippet] + mcp_tools
+
+        # Define snippet fixer subagent - handles ALL testing and fixing
+        # This subagent gets access to MCP tools for fetching LangChain docs
+        snippet_fixer_subagent = {
+            "name": "snippet_fixer",
+            "description": "Test and fix a code snippet, returning the working version",
+            "system_prompt": """You are a CONSERVATIVE code testing and fixing specialist.
+
+Your job:
+1. Test the code snippet by calling execute_code_snippet(code, language, dependencies)
+2. If it passes → return the original code UNCHANGED with success=true
+3. If it fails → make ONLY the minimum necessary fix, test again (max 3 attempts)
+4. Return the final result
+
+You MUST return a JSON response:
+{
+  "success": true/false,
+  "original_code": "...",
+  "fixed_code": "...",  // same as original if it worked first try
+  "error": "..."  // empty if success
+}
+
+CRITICAL RULES - READ CAREFULLY:
+1. **MINIMAL CHANGES ONLY**: Fix ONLY what's broken. Do NOT:
+   - Rewrite working code
+   - Change comments, docstrings, or formatting (unless syntax errors)
+   - "Improve" variable names
+   - Add type hints
+   - Refactor working logic
+   - Change coding style
+
+2. **SURGICAL FIXES**: Only fix these specific issues:
+   - Import errors (wrong module names, missing imports)
+   - Syntax errors (typos, wrong syntax)
+   - API errors (deprecated methods, wrong parameters)
+   - Runtime errors (wrong types, missing attributes)
+
+3. **PRESERVE EVERYTHING ELSE**:
+   - Keep all comments exactly as-is
+   - Keep docstrings exactly as-is
+   - Keep variable names exactly as-is
+   - Keep code structure exactly as-is
+   - Keep whitespace/formatting exactly as-is
+   - NOTE: Python code will be automatically formatted with ruff after you're done
+
+4. **DEPENDENCIES**: Analyze imports to detect dependencies (pandas, numpy, langchain, etc)
+
+5. **GIVE UP**: After 3 fix attempts, return with success=false
+
+## LangChain Code Support:
+
+If the code uses LangChain/LangGraph and you encounter errors:
+- Use the 'fetch' tool to retrieve relevant documentation
+- Example: fetch(url="https://python.langchain.com/docs/concepts/...")
+- The docs can help you understand the correct API usage
+- Focus ONLY on fixing import errors, deprecated APIs, and incorrect usage patterns
+- DO NOT refactor or "improve" the code
+
+## Example of GOOD vs BAD fixes:
+
+❌ BAD (too many changes):
+```python
+# Original
+def foo(x):
+    # this does something
+    return x + 1
+
+# Bad fix (changed comment, renamed variable)
+def foo(input_value):
+    # This function increments the input value by 1
+    return input_value + 1
+```
+
+✅ GOOD (minimal fix):
+```python
+# Original (broken import)
+from langchain import ChatOpenAI  # Wrong in v0.3
+
+# Good fix (only fixed the import)
+from langchain_openai import ChatOpenAI  # Fixed import
+```
+
+Remember: Your goal is to make code WORK, not to make it "better".
+""",
+            "tools": subagent_tools,  # Subagent gets both execute_code_snippet and fetch tools
+            "model": self.llm,
+        }
+
+        # Main agent only gets base tools (no MCP tools)
+        all_tools = self.tools
+
+        # Create agent with checkpointer for conversation memory
+        from langgraph.checkpoint.memory import MemorySaver
+
+        self.agent = create_deep_agent(
+            model=self.llm,
+            tools=all_tools,  # Main agent has file/directory tools + MCP tools
+            system_prompt=self._get_system_prompt(has_mcp=False),  # Main agent doesn't get MCP tools
+            checkpointer=MemorySaver(),  # In-memory checkpointer for conversation state
+            context_schema=DocsReviewerState,  # Custom state schema with file cache
+            subagents=[snippet_fixer_subagent],  # Subagent has execute_code_snippet
+        )
+
+        return self
+
+    def _get_system_prompt(self, has_mcp: bool = False) -> str:
+        """
+        Get the system prompt for the CLI agent.
+
+        Args:
+            has_mcp: Whether MCP tools are available
+        """
+        base_prompt = f"""You are a documentation reviewer that validates and fixes code snippets in markdown files.
+
+Working directory: {self.current_working_directory}
+
+## Workflow:
+
+When asked to review a markdown file:
+1. Call `review_markdown_file(filepath)` - extracts all snippets
+2. Spawn a `snippet_fixer` subagent for EACH snippet (in parallel)
+3. Call `finalize_review([results])` with all subagent results
+
+Be direct and efficient - don't use extra tools like ls, find_markdown_files, or read_file unless explicitly needed.
+If given a file path, just review it immediately.
+
+## Spawning snippet_fixer subagents:
+
+```
+task(subagent_type="snippet_fixer", description="Test and fix: <code snippet>\\n\\nLanguage: <lang>")
+```
+
+The subagent returns: {{"success": bool, "original_code": str, "fixed_code": str, "error": str}}
+
+Then call finalize_review with ALL these results.
+"""
+        if has_mcp:
+            base_prompt += """
+
+## Web Fetch Tool (MCP):
+
+You have access to a 'fetch' tool that can retrieve and convert web pages to markdown.
+Use this to access LangChain documentation when:
+- You need information about LangChain APIs, classes, or functions
+- You're reviewing code that uses LangChain
+- You need examples or best practices for LangChain usage
+
+Example URLs:
+- https://python.langchain.com/docs/ - Main LangChain docs
+- https://langchain-ai.github.io/langgraph/ - LangGraph docs
+
+The fetch tool converts HTML to markdown for easier processing.
+"""
+        return base_prompt
+
+    def _create_cli_tools(self) -> list:
+        """Create tools for the CLI agent."""
+        tools = []
+
+        @tool
+        def review_markdown_file(markdown_file: str) -> str:
+            """
+            Extract code snippets from a markdown file.
+
+            IMPORTANT: After calling this tool, spawn a snippet_fixer subagent for EACH
+            snippet using the task tool. Do this IN PARALLEL for speed.
+
+            Args:
+                markdown_file: Path to the markdown file to review
+
+            Returns:
+                List of snippets with their code and metadata
+            """
+            try:
+                file_path = Path(markdown_file)
+                if not file_path.is_absolute():
+                    file_path = self.current_working_directory / file_path
+
+                if not file_path.exists():
+                    return f"Error: File not found: {file_path}"
+
+                # Extract snippets
+                snippets = extract_code_snippets(file_path)
+                if not snippets:
+                    return "No code snippets found in file"
+
+                # Get executable snippets
+                executable = filter_executable_snippets(snippets)
+                if not executable:
+                    return "No executable code snippets found in this file"
+
+                # Store context for finalize_review
+                self._review_context = {
+                    "file_path": file_path,
+                    "snippets": snippets,
+                    "executable": executable,
+                }
+
+                # Create line number mapping for display
+                self._code_line_map = {
+                    s['code']: f"lines {s['start_line']}-{s['end_line']}"
+                    for s in executable
+                }
+
+                # Return snippet details with code preview
+                snippet_details = []
+                for i, s in enumerate(executable):
+                    # Get first 10 lines of code
+                    code_lines = s['code'].split('\n')[:10]
+                    code_preview = '\n'.join(code_lines)
+                    if len(s['code'].split('\n')) > 10:
+                        code_preview += '\n...'
+
+                    snippet_details.append(
+                        f"Snippet {i+1} ({s['language']}, lines {s['start_line']}-{s['end_line']}):\n{code_preview}"
+                    )
+
+                return f"""Found {len(executable)} executable snippets in {file_path.name}:
+
+{chr(10).join(snippet_details)}
+
+NEXT: Spawn a snippet_fixer subagent for EACH snippet (all in parallel).
+After all complete, call finalize_review with ALL the results."""
+            except Exception as e:
+                return f"Error: {str(e)}"
+
+        @tool
+        def finalize_review(snippet_results: list[dict]) -> str:
+            """
+            Apply fixes to the markdown file and show diff.
+
+            Call this AFTER testing/fixing all snippets.
+
+            Args:
+                snippet_results: List of results for each snippet, in order.
+                                Each dict should have:
+                                - "original_code": str - original snippet code
+                                - "fixed_code": str - fixed code (or same as original if it worked)
+                                - "success": bool - whether the final version works
+                                - "error": str - error message if failed
+
+            Returns:
+                JSON string with diff and summary statistics
+            """
+            import json
+
+            try:
+                if not hasattr(self, '_review_context'):
+                    return json.dumps({"error": "No active review context. Call review_markdown_file first."})
+
+                context = self._review_context
+                file_path = context["file_path"]
+                snippets = context["snippets"]
+                executable = context["executable"]
+
+                # Build results from snippet_results
+                results = []
+                for i, (snippet, result_dict) in enumerate(zip(executable, snippet_results)):
+                    # Extract data from result dict
+                    fixed_code = result_dict.get("fixed_code", snippet["code"])
+                    success = result_dict.get("success", False)
+                    error = result_dict.get("error", "")
+
+                    # Build analysis
+                    if success:
+                        analysis = f"✓ Success"
+                        if fixed_code != snippet["code"]:
+                            analysis += " (code was fixed)"
+                    else:
+                        analysis = f"✗ Failed\nError: {error}"
+
+                    results.append({
+                        "snippet_index": i,
+                        "success": success,
+                        "analysis": analysis,
+                        "original_code": snippet["code"],
+                        "corrected_code": fixed_code,  # Use the fixed code
+                        "language": snippet["language"],
+                        "start_line": snippet["start_line"],
+                        "end_line": snippet["end_line"],
+                    })
+
+                # Write corrected markdown with inline editing
+                diff_info = write_corrected_markdown(
+                    file_path,
+                    file_path,  # Output path (ignored in inline mode)
+                    snippets,
+                    results,
+                    inline=True  # Enable inline editing
+                )
+
+                # Calculate stats
+                total = len(results)
+                successful = sum(1 for r in results if r["success"])
+                failed = total - successful
+
+                # Clear context
+                del self._review_context
+
+                return json.dumps({
+                    "success": True,
+                    "file": str(file_path),
+                    "total_snippets": total,
+                    "successful": successful,
+                    "failed": failed,
+                    "success_rate": f"{(successful / total * 100):.1f}%" if total > 0 else "0%",
+                    "has_changes": diff_info["has_changes"],
+                    "diff": diff_info["diff"],
+                })
+            except Exception as e:
+                return json.dumps({"error": str(e)})
+
+        @tool
+        def change_directory(directory: str) -> dict[str, Any]:
+            """
+            Change the current working directory.
+
+            Args:
+                directory: Path to the new working directory
+
+            Returns:
+                Dictionary with the new working directory
+            """
+            try:
+                new_dir = Path(directory)
+                if not new_dir.is_absolute():
+                    new_dir = self.current_working_directory / new_dir
+
+                if not new_dir.exists():
+                    return {"error": f"Directory not found: {new_dir}"}
+
+                if not new_dir.is_dir():
+                    return {"error": f"Not a directory: {new_dir}"}
+
+                self.current_working_directory = new_dir.resolve()
+                return {
+                    "success": True,
+                    "working_directory": str(self.current_working_directory),
+                }
+            except Exception as e:
+                return {"error": str(e)}
+
+        @tool
+        def get_working_directory() -> str:
+            """
+            Get the current working directory.
+
+            Returns:
+                The current working directory path
+            """
+            return str(self.current_working_directory)
+
+        @tool
+        def find_markdown_files(directory: Optional[str] = None) -> dict[str, Any]:
+            """
+            Find all markdown files in a directory.
+
+            Args:
+                directory: Directory to search (default: current working directory)
+
+            Returns:
+                Dictionary with list of markdown files found
+            """
+            try:
+                search_dir = Path(directory) if directory else self.current_working_directory
+                if not search_dir.is_absolute():
+                    search_dir = self.current_working_directory / search_dir
+
+                if not search_dir.exists():
+                    return {"error": f"Directory not found: {search_dir}"}
+
+                md_files = list(search_dir.rglob("*.md"))
+                return {
+                    "directory": str(search_dir),
+                    "count": len(md_files),
+                    "files": [str(f.relative_to(search_dir)) for f in md_files],
+                }
+            except Exception as e:
+                return {"error": str(e)}
+
+        tools.append(review_markdown_file)
+        tools.append(finalize_review)
+        tools.append(change_directory)
+        tools.append(get_working_directory)
+        tools.append(find_markdown_files)
+
+        return tools
+
+    def _display_tool_event(self, event_data: dict, console):
+        """Display a custom tool event."""
+        from rich.syntax import Syntax
+
+        event_kind = event_data.get("kind")
+
+        if event_kind == "snippet_extracted":
+            count = event_data.get("count", 0)
+            file = event_data.get("file", "")
+            console.print(f"[#2f6868]  ✅ Found {count} executable snippet{'s' if count != 1 else ''} in {file}[/#2f6868]")
+
+        elif event_kind == "snippet_detail":
+            num = event_data.get("num")
+            lang = event_data.get("language")
+            lines = event_data.get("lines")
+            code = event_data.get("code", "")
+            console.print(f"\n  [cyan]Snippet {num} ({lang}, {lines}):[/cyan]")
+            for line in code.split('\n')[:10]:
+                console.print(f"    [dim]{line}[/dim]")
+            if len(code.split('\n')) > 10:
+                console.print(f"    [dim]...[/dim]")
+
+        elif event_kind == "code_execution":
+            lang = event_data.get("language")
+            lines = event_data.get("lines")
+            success = event_data.get("success")
+            output = event_data.get("output", "")
+            error = event_data.get("error", "")
+
+            status = "✓" if success else "✗"
+            color = "#2f6868" if success else "red"
+            result = output[:100] if success else error.split('\n')[-1][:80]
+
+            console.print(f"\n[#beb4fd]🔧 execute_code_snippet[/#beb4fd] [dim]→[/dim] [{color}]{status} {lang}[/{color}] [dim]{lines} → {repr(result)}...[/dim]")
+
+        elif event_kind == "subagent_spawn":
+            subagent = event_data.get("subagent", "unknown")
+            snippet_info = event_data.get("snippet_info", "")
+            console.print(f"\n[yellow]🤖 Spawning subagent:[/yellow] [cyan]{subagent}[/cyan] [dim]{snippet_info}[/dim]")
+
+        elif event_kind == "subagent_complete":
+            subagent = event_data.get("subagent", "unknown")
+            success = event_data.get("success")
+            status = "✅" if success else "❌"
+            console.print(f"[yellow]{status} Subagent complete:[/yellow] [cyan]{subagent}[/cyan]")
+
+    async def process_message(self, message: str, console) -> str:
+        """
+        Process a user message with streaming output.
+
+        Args:
+            message: User's message
+            console: Rich console for output
+
+        Returns:
+            Agent's response as a string
+        """
+        import json
+        from rich.syntax import Syntax
+
+        # Ensure agent is initialized
+        if self.agent is None:
+            await self.async_init()
+
+        # Stream the agent's response
+        try:
+            final_response = ""
+            shown_todos = set()
+            last_ai_message = None
+            # Track tool call details for displaying with results
+            tool_call_details = {}  # Maps tool_call_id -> (tool_name, args)
+
+            # Use "values" stream mode to get the full state including all messages
+            # We'll only display messages after self.last_message_count
+            for state in self.agent.stream(
+                {"messages": [HumanMessage(content=message)]},
+                config={"configurable": {"thread_id": self.thread_id}},
+                stream_mode="values",
+            ):
+                # state contains the full graph state including all messages
+                if not isinstance(state, dict) or "messages" not in state:
+                    continue
+
+                messages = state["messages"]
+
+                # If message count decreased, summarization happened - reset counter
+                if len(messages) < self.last_message_count:
+                    self.last_message_count = 0
+
+                # Only process messages we haven't seen yet
+                new_messages = messages[self.last_message_count:]
+
+                for msg in new_messages:
+                    # Skip human messages (never display them)
+                    if hasattr(msg, 'type') and msg.type == 'human':
+                        self.last_message_count += 1
+                        continue
+
+                    # Handle AI messages
+                    if hasattr(msg, 'type') and msg.type == 'ai':
+                        last_ai_message = msg
+                        self.last_message_count += 1
+
+                        # Check if this AI message has text content (not just tool calls)
+                        has_content = False
+                        if hasattr(msg, 'content'):
+                            content = msg.content
+                            if isinstance(content, str) and content.strip():
+                                has_content = True
+                                # Print the AI's text content
+                                console.print(content, end="")
+                                final_response += content
+                            elif isinstance(content, list):
+                                # Handle Anthropic content blocks format
+                                for block in content:
+                                    if isinstance(block, dict) and block.get('type') == 'text':
+                                        text = block.get('text', '')
+                                        if text:
+                                            has_content = True
+                                            console.print(text, end="")
+                                            final_response += text
+
+                        # Check for tool calls
+                        if hasattr(msg, 'tool_calls') and msg.tool_calls:
+                            for tool_call in msg.tool_calls:
+                                tool_id = tool_call.get('id', '') or str(tool_call)
+                                tool_name = tool_call.get('name', 'unknown')
+                                tool_args = tool_call.get('args', {})
+
+                                # Store tool call details for result display
+                                tool_call_details[tool_id] = (tool_name, tool_args)
+
+                                # For execute_code_snippet, DON'T print anything yet - wait for result
+                                # This prevents the display from showing tool call then result separately
+                                if tool_name == "execute_code_snippet":
+                                    continue
+
+                                # Add newline if we just printed content
+                                if has_content and final_response and not final_response.endswith('\n'):
+                                    console.print()
+
+                                # Special formatting for task (subagent spawning)
+                                if tool_name == "task":
+                                    subagent = tool_args.get('subagent_type', 'unknown')
+                                    description = tool_args.get('description', '')
+
+                                    # Extract language and code preview for snippet_fixer
+                                    if subagent == "snippet_fixer":
+                                        # Try to parse out the language
+                                        lang = "code"
+                                        if "Language:" in description:
+                                            lang_part = description.split("Language:")[-1].strip().split()[0].strip()
+                                            if lang_part:
+                                                lang = lang_part
+
+                                        # Get source file info from the context if available
+                                        source_info = ""
+                                        if hasattr(self, '_review_context'):
+                                            file_path = self._review_context.get('file_path')
+                                            if file_path:
+                                                source_info = f" from {file_path.name}"
+
+                                        # Extract first 5 lines of code
+                                        lines = description.split('\n')
+                                        code_lines = []
+                                        for line in lines:
+                                            if line.strip() and not line.startswith('Test and fix') and not line.startswith('Language'):
+                                                code_lines.append(line)
+                                                if len(code_lines) >= 5:
+                                                    break
+
+                                        # Display the snippet info
+                                        console.print(f"\n[yellow]🔧 Testing {lang} snippet{source_info}:[/yellow]")
+                                        for code_line in code_lines:
+                                            console.print(f"  [dim]{code_line}[/dim]")
+                                        if len(code_lines) >= 5:
+                                            console.print(f"  [dim]...[/dim]")
+                                    else:
+                                        # Generic subagent display
+                                        desc_preview = description[:60] + "..." if len(description) > 60 else description
+                                        console.print(f"\n[yellow]🤖 Spawning:[/yellow] [cyan]{subagent}[/cyan] [dim]{desc_preview}[/dim]")
+                                    continue
+
+                                # For other tools, show them immediately
+                                console.print(f"\n[#beb4fd]🔧 {tool_name}[/#beb4fd]")
+                                # Show args if they're concise
+                                if tool_args:
+                                    for key, value in tool_args.items():
+                                        if isinstance(value, str) and len(value) > 60:
+                                            console.print(f"  [dim]{key}:[/dim] {value[:60]}...")
+                                        elif isinstance(value, (list, dict)):
+                                            # Skip verbose data structures
+                                            continue
+                                        else:
+                                            console.print(f"  [dim]{key}:[/dim] {value}")
+
+                    # Handle tool response messages
+                    elif hasattr(msg, 'type') and msg.type == 'tool':
+                        self.last_message_count += 1
+                        tool_call_id = getattr(msg, 'tool_call_id', None)
+
+                        # Get tool name and args from stored details
+                        tool_name = 'unknown'
+                        tool_args = {}
+                        if tool_call_id in tool_call_details:
+                            tool_name, tool_args = tool_call_details[tool_call_id]
+
+                        tool_content = getattr(msg, 'content', '')
+
+                        # Parse and display tool results
+                        try:
+                            result = json.loads(tool_content) if isinstance(tool_content, str) else tool_content
+
+                            # Special formatting for execute_code_snippet (handle first, before generic error check)
+                            if tool_name == "execute_code_snippet":
+                                # Result is a JSON string
+                                try:
+                                    exec_result = json.loads(result) if isinstance(result, str) else result
+
+                                    # Get language and line info from stored tool args
+                                    language = tool_args.get('language', 'unknown')
+                                    code = tool_args.get('code', '')
+                                    line_info = None
+                                    if hasattr(self, '_code_line_map') and code in self._code_line_map:
+                                        line_info = self._code_line_map[code]
+
+                                    # Show as a combined tool call + result (inline style)
+                                    if exec_result.get("success"):
+                                        output = exec_result.get("output", "")
+                                        preview = output[:100] + "..." if len(output) > 100 else output
+                                        # Show on single line: "🔧 execute_code_snippet → ✓ python (lines X-Y) → 'output'"
+                                        console.print(f"\n[#beb4fd]🔧 execute_code_snippet[/#beb4fd] [dim]→[/dim] [#2f6868]✓ {language}[/#2f6868]", end="")
+                                        if line_info:
+                                            console.print(f" [dim]{line_info}[/dim]", end="")
+                                        if preview.strip():
+                                            console.print(f" [dim]→[/dim] {repr(preview)}")
+                                        else:
+                                            console.print()
+                                    else:
+                                        error = exec_result.get("error", "Unknown error")
+                                        # Show last line of error (usually most useful)
+                                        error_lines = error.split('\n')
+                                        error_preview = error_lines[-1].strip() if error_lines else error
+                                        if len(error_preview) > 80:
+                                            error_preview = error_preview[:80] + "..."
+                                        console.print(f"\n[#beb4fd]🔧 execute_code_snippet[/#beb4fd] [dim]→[/dim] [red]✗ {language}[/red]", end="")
+                                        if line_info:
+                                            console.print(f" [dim]{line_info}[/dim]", end="")
+                                        console.print(f" [dim]→[/dim] {error_preview}")
+                                except (json.JSONDecodeError, TypeError):
+                                    console.print(f"\n[#beb4fd]🔧 execute_code_snippet[/#beb4fd] [dim]→[/dim] [#2f6868]✓[/#2f6868]")
+                                continue
+
+                            # Check for errors in other tools
+                            if isinstance(result, dict) and "error" in result:
+                                console.print(f"[red]  ✗ Error: {result['error']}[/red]\n")
+                                continue
+
+                            # Special formatting for task (subagent) results
+                            if tool_name == "task" and isinstance(result, str):
+                                # Show subagent completion
+                                subagent_name = "unknown"
+                                if tool_call_id in tool_call_details:
+                                    _, args = tool_call_details[tool_call_id]
+                                    subagent_name = args.get('subagent_type', 'unknown')
+
+                                # For snippet_fixer, parse the result to show status
+                                if subagent_name == "snippet_fixer":
+                                    try:
+                                        import json
+                                        snippet_result = json.loads(result) if isinstance(result, str) else result
+                                        success = snippet_result.get("success", False)
+                                        status_icon = "✓" if success else "✗"
+                                        status_color = "#2f6868" if success else "red"
+                                        status_text = "passed" if success else "failed"
+                                        console.print(f"[{status_color}]  {status_icon} Snippet {status_text}[/{status_color}]")
+                                    except:
+                                        console.print(f"[#2f6868]  ✓ Snippet complete[/#2f6868]")
+                                else:
+                                    # Generic subagent result
+                                    console.print(f"\n[yellow]✅ Subagent complete:[/yellow] [cyan]{subagent_name}[/cyan]")
+                                    result_preview = result[:200] + "..." if len(result) > 200 else result
+                                    console.print(f"  [dim]{result_preview}[/dim]\n")
+                                continue
+
+                            # Special formatting for review_markdown_file - show snippets
+                            if tool_name == "review_markdown_file" and isinstance(result, str):
+                                # Parse snippet info and display nicely
+                                if "Found" in result and "snippets" in result:
+                                    console.print(f"[#2f6868]  ✅ {result.split(chr(10))[0]}[/#2f6868]")
+                                    # Display code snippets with syntax highlighting
+                                    lines = result.split('\n')
+                                    for line in lines[2:]:  # Skip first two lines
+                                        if line.startswith('Snippet'):
+                                            console.print(f"\n  [cyan]{line}[/cyan]")
+                                        elif line.strip() and not line.startswith('NEXT'):
+                                            console.print(f"    [dim]{line}[/dim]")
+                                    console.print()
+                                continue
+
+                            # Special formatting for finalize_review - show diff
+                            if tool_name == "finalize_review":
+                                try:
+                                    review_result = json.loads(result) if isinstance(result, str) else result
+
+                                    if review_result.get("success"):
+                                        total = review_result.get("total_snippets", 0)
+                                        successful = review_result.get("successful", 0)
+                                        failed = review_result.get("failed", 0)
+
+                                        console.print(f"[#2f6868]  ✅ Review finalized[/#2f6868]")
+                                        console.print(f"    [dim]{successful}/{total} passed[/dim]", end="")
+                                        if failed > 0:
+                                            console.print(f", [yellow]{failed} failed[/yellow]")
+                                        else:
+                                            console.print()
+
+                                        # Show diff if there are changes
+                                        if review_result.get("has_changes") and review_result.get("diff"):
+                                            console.print(f"\n[dim]Changes:[/dim]")
+                                            # Display diff with gentle colors
+                                            diff_text = review_result["diff"]
+                                            for line in diff_text.split('\n'):
+                                                if line.startswith('---') or line.startswith('+++'):
+                                                    console.print(f"[dim]{line}[/dim]")
+                                                elif line.startswith('@@'):
+                                                    console.print(f"[cyan]{line}[/cyan]")
+                                                elif line.startswith('+'):
+                                                    console.print(f"[#22863a]{line}[/#22863a]")  # Gentle green
+                                                elif line.startswith('-'):
+                                                    console.print(f"[#b31d28]{line}[/#b31d28]")  # Gentle red
+                                                else:
+                                                    console.print(f"[dim]{line}[/dim]")
+                                        else:
+                                            console.print(f"    [dim]No changes needed[/dim]")
+                                        console.print()
+                                except (json.JSONDecodeError, TypeError):
+                                    console.print(f"[#2f6868]  ✅[/#2f6868]\n")
+                                continue
+
+                            # Special formatting for find_markdown_files
+                            if tool_name == "find_markdown_files" and isinstance(result, dict):
+                                count = result.get("count", 0)
+                                console.print(f"[#2f6868]  ✅ Found {count} markdown file{'s' if count != 1 else ''}[/#2f6868]")
+                                files = result.get("files", [])
+                                if files:
+                                    for f in files[:10]:  # Show first 10
+                                        console.print(f"    [dim]•[/dim] {f}")
+                                    if len(files) > 10:
+                                        console.print(f"    [dim]... and {len(files) - 10} more[/dim]")
+                                console.print()
+                                continue
+
+                            # Generic success for other tools
+                            if isinstance(result, dict) and result.get("success"):
+                                console.print(f"[#2f6868]  ✅ {tool_name}[/#2f6868]\n")
+                            # For tools that return simple values (strings, etc), don't show anything
+                            # They already showed the tool call, that's enough
+
+                        except (json.JSONDecodeError, TypeError):
+                            # Non-JSON response, don't show anything - tool call was already shown
+                            pass
+
+                # Handle todos from state
+                if "todos" in state:
+                    todos = state["todos"]
+                    if todos:
+                        todos_hash = str([(t.get("content"), t.get("status")) for t in todos])
+                        if todos_hash not in shown_todos:
+                            shown_todos.add(todos_hash)
+
+                            if final_response and not final_response.endswith('\n'):
+                                console.print()
+
+                            console.print("\n[dim]Tasks:[/dim]")
+                            for todo in todos:
+                                status = todo.get("status", "pending")
+                                status_emoji = {"pending": "○", "in_progress": "◐", "completed": "●"}.get(status, "•")
+                                todo_content = todo.get("content", "Unknown task")
+
+                                if status == "completed":
+                                    console.print(f"  [dim]{status_emoji} {todo_content}[/dim]")
+                                elif status == "in_progress":
+                                    active_form = todo.get("activeForm", todo_content)
+                                    console.print(f"  {status_emoji} {active_form}")
+                                else:
+                                    console.print(f"  [dim]{status_emoji} {todo_content}[/dim]")
+                            console.print()
+
+            # Ensure final newline
+            if final_response and not final_response.endswith('\n'):
+                console.print()
+
+            # Conversation history is automatically managed by LangGraph checkpointer
+            return final_response
+
+        except Exception as e:
+            error_message = f"Error: {str(e)}"
+            console.print(f"\n[red]{error_message}[/red]")
+            return error_message
+
+    def reset_conversation(self) -> None:
+        """Reset the conversation history by creating a new thread."""
+        import uuid
+        self.thread_id = f"docs-reviewer-{uuid.uuid4().hex[:8]}"
diff --git a/docs_reviewer/docs_reviewer/markdown_parser.py b/docs_reviewer/docs_reviewer/markdown_parser.py
new file mode 100644
index 00000000..add7ff0f
--- /dev/null
+++ b/docs_reviewer/docs_reviewer/markdown_parser.py
@@ -0,0 +1,146 @@
+"""Markdown parsing utilities for extracting code snippets."""
+
+import re
+from pathlib import Path
+from typing import TypedDict
+
+
+class CodeSnippet(TypedDict):
+    """Represents a code snippet extracted from markdown."""
+
+    language: str
+    code: str
+    start_line: int
+    end_line: int
+    metadata: dict[str, str]  # Additional metadata from code fence
+
+
+def extract_code_snippets(markdown_file: Path) -> list[CodeSnippet]:
+    """
+    Extract all code snippets from a markdown file.
+
+    Args:
+        markdown_file: Path to the markdown file
+
+    Returns:
+        List of code snippets with their metadata
+    """
+    with open(markdown_file, encoding="utf-8") as f:
+        content = f.read()
+
+    snippets: list[CodeSnippet] = []
+    lines = content.split("\n")
+
+    # Pattern to match code fence openings with optional metadata
+    # Examples: ```python, ```python {title="example.py"}, ```py
+    fence_pattern = re.compile(r"^```(\w+)?\s*(\{[^}]*\})?\s*$")
+
+    i = 0
+    while i < len(lines):
+        line = lines[i]
+        match = fence_pattern.match(line)
+
+        if match:
+            language = match.group(1) or "text"
+            metadata_str = match.group(2) or "{}"
+
+            # Parse metadata if present
+            metadata: dict[str, str] = {}
+            if metadata_str != "{}":
+                # Simple parsing of {key="value"} style metadata
+                metadata_pattern = re.compile(r'(\w+)="([^"]*)"')
+                metadata = dict(metadata_pattern.findall(metadata_str))
+
+            start_line = i + 1
+            code_lines = []
+
+            # Find the closing fence
+            i += 1
+            while i < len(lines) and not lines[i].strip().startswith("```"):
+                code_lines.append(lines[i])
+                i += 1
+
+            end_line = i
+
+            code = "\n".join(code_lines)
+
+            snippets.append(
+                CodeSnippet(
+                    language=language,
+                    code=code,
+                    start_line=start_line,
+                    end_line=end_line,
+                    metadata=metadata,
+                )
+            )
+
+        i += 1
+
+    return snippets
+
+
+def filter_executable_snippets(snippets: list[CodeSnippet]) -> list[CodeSnippet]:
+    """
+    Filter snippets to only include executable code.
+
+    Excludes snippets that are:
+    - Plain text or markdown
+    - Shell output examples
+    - Snippets marked as non-executable in metadata
+
+    Args:
+        snippets: List of all code snippets
+
+    Returns:
+        List of executable code snippets
+    """
+    executable_languages = {
+        "python",
+        "py",
+        "javascript",
+        "js",
+        "typescript",
+        "ts",
+        "bash",
+        "sh",
+        "shell",
+    }
+
+    filtered = []
+    for snippet in snippets:
+        # Skip if language is not executable
+        if snippet["language"].lower() not in executable_languages:
+            continue
+
+        # Skip if metadata indicates non-executable
+        if snippet["metadata"].get("exec") == "false":
+            continue
+
+        # Skip if metadata indicates it's output
+        if snippet["metadata"].get("output") == "true":
+            continue
+
+        filtered.append(snippet)
+
+    return filtered
+
+
+def categorize_snippets(snippets: list[CodeSnippet]) -> dict[str, list[CodeSnippet]]:
+    """
+    Categorize snippets by language.
+
+    Args:
+        snippets: List of code snippets
+
+    Returns:
+        Dictionary mapping language to list of snippets
+    """
+    categories: dict[str, list[CodeSnippet]] = {}
+
+    for snippet in snippets:
+        lang = snippet["language"].lower()
+        if lang not in categories:
+            categories[lang] = []
+        categories[lang].append(snippet)
+
+    return categories
diff --git a/docs_reviewer/docs_reviewer/markdown_writer.py b/docs_reviewer/docs_reviewer/markdown_writer.py
new file mode 100644
index 00000000..ed4c9150
--- /dev/null
+++ b/docs_reviewer/docs_reviewer/markdown_writer.py
@@ -0,0 +1,208 @@
+"""Markdown writing utilities for creating corrected documentation."""
+
+from pathlib import Path
+from typing import Any
+from datetime import datetime
+import difflib
+
+from docs_reviewer.markdown_parser import CodeSnippet
+
+
+def write_corrected_markdown(
+    original_file: Path,
+    output_file: Path,
+    snippets: list[CodeSnippet],
+    results: list[dict[str, Any]],
+    inline: bool = True,
+) -> dict[str, Any]:
+    """
+    Write a corrected markdown file with updated code snippets.
+
+    Args:
+        original_file: Path to the original markdown file
+        output_file: Path to write the corrected markdown (ignored if inline=True)
+        snippets: List of original code snippets
+        results: List of review results for each snippet
+        inline: If True, edits the original file in place. If False, creates a new file.
+
+    Returns:
+        Dictionary with diff information
+    """
+    # Read the original file
+    with open(original_file, encoding="utf-8") as f:
+        original_lines = f.readlines()
+
+    # Create a mapping of snippet indices to results
+    result_map = {r["snippet_index"]: r for r in results}
+
+    # Track which lines need to be replaced
+    replacements: dict[int, list[str]] = {}
+    has_changes = False
+
+    for i, snippet in enumerate(snippets):
+        if i not in result_map:
+            continue
+
+        result = result_map[i]
+
+        # Only replace if there were corrections
+        if result["corrected_code"] != result["original_code"] or not result["success"]:
+            has_changes = True
+            start_line = snippet["start_line"] - 1  # Convert to 0-indexed
+            end_line = snippet["end_line"]
+
+            # Build the replacement content
+            replacement_lines = []
+
+            # Add a comment about the correction if snippet failed
+            if not result["success"]:
+                replacement_lines.append(f"<!-- REVIEW NOTE: This snippet had issues -->\n")
+                replacement_lines.append(f"<!-- {_escape_html_comment(result['analysis'][:200])} -->\n")
+                replacement_lines.append("\n")
+
+            # Add the code fence
+            replacement_lines.append(f"```{snippet['language']}\n")
+            replacement_lines.append(result["corrected_code"])
+            if not result["corrected_code"].endswith("\n"):
+                replacement_lines.append("\n")
+            replacement_lines.append("```\n")
+
+            replacements[start_line] = replacement_lines
+
+    # Build the corrected content
+    corrected_lines = []
+    i = 0
+    while i < len(original_lines):
+        if i in replacements:
+            # Write the replacement
+            for line in replacements[i]:
+                corrected_lines.append(line)
+
+            # Skip the original code block
+            # Find the closing ```
+            i += 1
+            while i < len(original_lines) and not original_lines[i].strip().startswith("```"):
+                i += 1
+            i += 1  # Skip the closing ```
+        else:
+            corrected_lines.append(original_lines[i])
+            i += 1
+
+    # Determine output path
+    if inline:
+        target_file = original_file
+    else:
+        target_file = output_file
+
+    # Write the corrected file
+    with open(target_file, "w", encoding="utf-8") as f:
+        f.writelines(corrected_lines)
+
+    # Generate a minimalistic diff showing only the changes
+    diff_lines = _generate_minimalistic_diff(original_lines, corrected_lines, original_file, target_file)
+
+    return {
+        "has_changes": has_changes,
+        "diff": '\n'.join(diff_lines),
+        "target_file": str(target_file),
+    }
+
+
+def _generate_minimalistic_diff(original_lines: list[str], corrected_lines: list[str],
+                                  original_file: Path, target_file: Path) -> list[str]:
+    """Generate a clean, minimalistic diff focusing on actual changes."""
+    import difflib
+
+    # Get the diff blocks
+    matcher = difflib.SequenceMatcher(None, original_lines, corrected_lines)
+    diff_output = []
+
+    diff_output.append(f"--- {original_file}")
+    diff_output.append(f"+++ {target_file}")
+
+    for tag, i1, i2, j1, j2 in matcher.get_opcodes():
+        if tag == 'equal':
+            continue
+
+        # Add location marker
+        diff_output.append(f"@@ -{i1+1},{i2-i1} +{j1+1},{j2-j1} @@")
+
+        # Show removed lines (non-empty only, or keep one empty to show the change)
+        removed_lines = original_lines[i1:i2]
+        non_empty_removed = [l for l in removed_lines if l.strip()]
+        if non_empty_removed or (removed_lines and not corrected_lines[j1:j2]):
+            for line in removed_lines:
+                diff_output.append(f"-{line.rstrip()}")
+
+        # Show added lines (non-empty only, or keep one empty to show the change)
+        added_lines = corrected_lines[j1:j2]
+        non_empty_added = [l for l in added_lines if l.strip()]
+        if non_empty_added or (added_lines and not removed_lines):
+            for line in added_lines:
+                diff_output.append(f"+{line.rstrip()}")
+
+    return diff_output
+
+
+def write_review_report(
+    output_file: Path,
+    markdown_file: Path,
+    snippets: list[CodeSnippet],
+    results: list[dict[str, Any]],
+) -> None:
+    """
+    Write a detailed review report.
+
+    Args:
+        output_file: Path to write the report
+        markdown_file: Path to the reviewed markdown file
+        snippets: List of code snippets
+        results: List of review results
+    """
+    with open(output_file, "w", encoding="utf-8") as f:
+        f.write(f"# Docs Review Report\n\n")
+        f.write(f"**File:** {markdown_file}\n")
+        f.write(f"**Date:** {datetime.now().isoformat()}\n\n")
+
+        # Summary statistics
+        total = len(results)
+        successful = sum(1 for r in results if r["success"])
+        failed = total - successful
+
+        f.write(f"## Summary\n\n")
+        f.write(f"- Total snippets reviewed: {total}\n")
+        f.write(f"- Successful: {successful}\n")
+        f.write(f"- Failed: {failed}\n")
+        f.write(f"- Success rate: {(successful / total * 100):.1f}%\n\n")
+
+        # Detailed results
+        f.write(f"## Detailed Results\n\n")
+
+        for i, result in enumerate(results, 1):
+            snippet = snippets[result["snippet_index"]]
+
+            status = "✅ Success" if result["success"] else "❌ Failed"
+            f.write(f"### Snippet {i}: {status}\n\n")
+            f.write(f"**Language:** {result['language']}\n")
+            f.write(f"**Lines:** {result['start_line']}-{result['end_line']}\n\n")
+
+            f.write(f"**Analysis:**\n")
+            f.write(f"{result['analysis']}\n\n")
+
+            if result["corrected_code"] != result["original_code"]:
+                f.write(f"**Original Code:**\n")
+                f.write(f"```{result['language']}\n")
+                f.write(result["original_code"])
+                f.write(f"\n```\n\n")
+
+                f.write(f"**Corrected Code:**\n")
+                f.write(f"```{result['language']}\n")
+                f.write(result["corrected_code"])
+                f.write(f"\n```\n\n")
+
+            f.write("---\n\n")
+
+
+def _escape_html_comment(text: str) -> str:
+    """Escape text for safe inclusion in HTML comments."""
+    return text.replace("--", "- -").replace("<!--", "< !--").replace("-->", "-- >")
diff --git a/docs_reviewer/pyproject.toml b/docs_reviewer/pyproject.toml
new file mode 100644
index 00000000..8f97dc76
--- /dev/null
+++ b/docs_reviewer/pyproject.toml
@@ -0,0 +1,34 @@
+[project]
+name = "docs-reviewer"
+version = "0.1.0"
+description = "AI-powered documentation code snippet reviewer built on DeepAgents"
+readme = "README.md"
+requires-python = ">=3.11,<4.0"
+dependencies = [
+    "deepagents",
+    "langchain-anthropic>=0.3.14",  # Required for code execution tool
+    "langchain-mcp-adapters>=0.1.0",  # MCP integration for LangChain docs
+    "typer>=0.9.0",
+    "rich>=13.0.0",
+    "pyyaml>=6.0",
+    "pydantic>=2.0.0",
+    "python-dotenv>=1.0.0",
+    "prompt-toolkit>=3.0.0",  # For command history with up/down arrows
+]
+
+[project.scripts]
+docs-reviewer = "docs_reviewer.cli:app"
+
+[tool.uv.sources]
+deepagents = { path = "../", editable = true }
+
+[project.optional-dependencies]
+dev = [
+    "pytest",
+    "pytest-cov",
+    "ruff",
+]
+
+[build-system]
+requires = ["setuptools>=73.0.0", "wheel"]
+build-backend = "setuptools.build_meta"
diff --git a/docs_reviewer/uv.lock b/docs_reviewer/uv.lock
new file mode 100644
index 00000000..41b45c2b
--- /dev/null
+++ b/docs_reviewer/uv.lock
@@ -0,0 +1,1301 @@
+version = 1
+revision = 2
+requires-python = ">=3.11, <4.0"
+
+[[package]]
+name = "annotated-types"
+version = "0.7.0"
+source = { registry = "https://pypi.org/simple" }
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diff --git a/hitl.md b/hitl.md
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--- /dev/null
+++ b/hitl.md
@@ -0,0 +1,452 @@
+---
+title: Human-in-the-loop
+---
+
+
+
+The Human-in-the-Loop (HITL) middleware lets you add human oversight to agent tool calls.
+When a model proposes an action that might require review — for example, writing to a file or executing SQL — the middleware can pause execution and wait for a decision.
+
+It does this by checking each tool call against a configurable policy. If intervention is needed, the middleware issues an @[interrupt] that halts execution. The graph state is saved using LangGraph’s [persistence layer](/oss/langgraph/persistence), so execution can pause safely and resume later.
+
+A human decision then determines what happens next: the action can be approved as-is (`approve`), modified before running (`edit`), or rejected with feedback (`reject`).
+
+## Interrupt decision types
+
+The middleware defines three built-in ways a human can respond to an interrupt:
+
+| Decision Type | Description                                                               | Example Use Case                                    |
+|---------------|---------------------------------------------------------------------------|-----------------------------------------------------|
+| ✅ `approve`   | The action is approved as-is and executed without changes.                | Send an email draft exactly as written              |
+| ✏️ `edit`     | The tool call is executed with modifications.                             | Change the recipient before sending an email        |
+| ❌ `reject`    | The tool call is rejected, with an explanation added to the conversation. | Reject an email draft and explain how to rewrite it |
+
+The available decision types for each tool depend on the policy you configure in `interrupt_on`.
+When multiple tool calls are paused at the same time, each action requires a separate decision.
+Decisions must be provided in the same order as the actions appear in the interrupt request.
+
+<Tip>
+When **editing** tool arguments, make changes conservatively. Significant modifications to the original arguments may cause the model to re-evaluate its approach and potentially execute the tool multiple times or take unexpected actions.
+</Tip>
+
+
+## Configuring interrupts
+
+To use HITL, add the middleware to the agent’s `middleware` list when creating the agent.
+
+You configure it with a mapping of tool actions to the decision types that are allowed for each action. The middleware will interrupt execution when a tool call matches an action in the mapping.
+
+:::python
+```python
+from langchain.agents import create_agent
+from langchain.agents.middleware import HumanInTheLoopMiddleware # [!code highlight]
+from langgraph.checkpoint.memory import InMemorySaver # [!code highlight]
+
+
+agent = create_agent(
+    model="openai:gpt-4o",
+    tools=[write_file_tool, execute_sql_tool, read_data_tool],
+    middleware=[
+        HumanInTheLoopMiddleware( # [!code highlight]
+            interrupt_on={
+                "write_file": True,  # All decisions (approve, edit, reject) allowed
+                "execute_sql": {"allowed_decisions": ["approve", "reject"]},  # No editing allowed
+                # Safe operation, no approval needed
+                "read_data": False,
+            },
+            # Prefix for interrupt messages - combined with tool name and args to form the full message
+            # e.g., "Tool execution pending approval: execute_sql with query='DELETE FROM...'"
+            # Individual tools can override this by specifying a "description" in their interrupt config
+            description_prefix="Tool execution pending approval",
+        ),
+    ],
+    # Human-in-the-loop requires checkpointing to handle interrupts.
+    # In production, use a persistent checkpointer like AsyncPostgresSaver.
+    checkpointer=InMemorySaver(),  # [!code highlight]
+)
+```
+:::
+
+:::js
+```ts
+import { createAgent, humanInTheLoopMiddleware } from "langchain"; // [!code highlight]
+import { MemorySaver } from "@langchain/langgraph"; // [!code highlight]
+
+const agent = createAgent({
+    model: "openai:gpt-4o",
+    tools: [writeFileTool, executeSQLTool, readDataTool],
+    middleware: [
+        humanInTheLoopMiddleware({
+            interruptOn: {
+                write_file: true, // All decisions (approve, edit, reject) allowed
+                execute_sql: {
+                    allowedDecisions: ["approve", "reject"],
+                    // No editing allowed
+                    description: "🚨 SQL execution requires DBA approval",
+                },
+                // Safe operation, no approval needed
+                read_data: false,
+            },
+            // Prefix for interrupt messages - combined with tool name and args to form the full message
+            // e.g., "Tool execution pending approval: execute_sql with query='DELETE FROM...'"
+            // Individual tools can override this by specifying a "description" in their interrupt config
+            descriptionPrefix: "Tool execution pending approval",
+        }),
+    ],
+    // Human-in-the-loop requires checkpointing to handle interrupts.
+    // In production, use a persistent checkpointer like AsyncPostgresSaver.
+    checkpointer: new MemorySaver(), // [!code highlight]
+});
+```
+:::
+
+
+<Info>
+    You must configure a checkpointer to persist the graph state across interrupts.
+    In production, use a persistent checkpointer like @[`AsyncPostgresSaver`]. For testing or prototyping, use @[`InMemorySaver`].
+
+    When invoking the agent, pass a `config` that includes the **thread ID** to associate execution with a conversation thread.
+    See the [LangGraph interrupts documentation](/oss/langgraph/interrupts) for details.
+</Info>
+
+
+## Responding to interrupts
+
+When you invoke the agent, it runs until it either completes or an interrupt is raised. An interrupt is triggered when a tool call matches the policy you configured in `interrupt_on`. In that case, the invocation result will include an `__interrupt__` field with the actions that require review. You can then present those actions to a reviewer and resume execution once decisions are provided.
+
+
+:::python
+```python
+from langgraph.types import Command
+
+# Human-in-the-loop leverages LangGraph's persistence layer.
+# You must provide a thread ID to associate the execution with a conversation thread,
+# so the conversation can be paused and resumed (as is needed for human review).
+config = {"configurable": {"thread_id": "some_id"}} # [!code highlight]
+# Run the graph until the interrupt is hit.
+result = agent.invoke(
+    {
+        "messages": [
+            {
+                "role": "user",
+                "content": "Delete old records from the database",
+            }
+        ]
+    },
+    config=config # [!code highlight]
+)
+
+# The interrupt contains the full HITL request with action_requests and review_configs
+print(result['__interrupt__'])
+# > [
+# >    Interrupt(
+# >       value={
+# >          'action_requests': [
+# >             {
+# >                'name': 'execute_sql',
+# >                'arguments': {'query': 'DELETE FROM records WHERE created_at < NOW() - INTERVAL \'30 days\';'},
+# >                'description': 'Tool execution pending approval\n\nTool: execute_sql\nArgs: {...}'
+# >             }
+# >          ],
+# >          'review_configs': [
+# >             {
+# >                'action_name': 'execute_sql',
+# >                'allowed_decisions': ['approve', 'reject']
+# >             }
+# >          ]
+# >       }
+# >    )
+# > ]
+
+
+# Resume with approval decision
+agent.invoke(
+    Command( # [!code highlight]
+        resume={"decisions": [{"type": "approve"}]}  # or "edit", "reject" [!code highlight]
+    ), # [!code highlight]
+    config=config # Same thread ID to resume the paused conversation
+)
+```
+:::
+
+:::js
+```typescript
+import { HumanMessage } from "@langchain/core/messages";
+import { Command } from "@langchain/langgraph";
+
+// You must provide a thread ID to associate the execution with a conversation thread,
+// so the conversation can be paused and resumed (as is needed for human review).
+const config = { configurable: { thread_id: "some_id" } }; // [!code highlight]
+
+// Run the graph until the interrupt is hit.
+const result = await agent.invoke(
+    {
+        messages: [new HumanMessage("Delete old records from the database")],
+    },
+    config // [!code highlight]
+);
+
+
+// The interrupt contains the full HITL request with action_requests and review_configs
+console.log(result.__interrupt__);
+// > [
+// >    Interrupt(
+// >       value: {
+// >          action_requests: [
+// >             {
+// >                name: 'execute_sql',
+// >                arguments: { query: 'DELETE FROM records WHERE created_at < NOW() - INTERVAL \'30 days\';' },
+// >                description: 'Tool execution pending approval\n\nTool: execute_sql\nArgs: {...}'
+// >             }
+// >          ],
+// >          review_configs: [
+// >             {
+// >                action_name: 'execute_sql',
+// >                allowed_decisions: ['approve', 'reject']
+// >             }
+// >          ]
+// >       }
+// >    )
+// > ]
+
+// Resume with approval decision
+await agent.invoke(
+    new Command({ // [!code highlight]
+        resume: { decisions: [{ type: "approve" }] }, // or "edit", "reject" [!code highlight]
+    }), // [!code highlight]
+    config // Same thread ID to resume the paused conversation
+);
+```
+:::
+
+### Decision types
+
+<Tabs>
+<Tab title="✅ approve">
+Use `approve` to approve the tool call as-is and execute it without changes.
+
+:::python
+```python
+agent.invoke(
+    Command(
+        # Decisions are provided as a list, one per action under review.
+        # The order of decisions must match the order of actions
+        # listed in the `__interrupt__` request.
+        resume={
+            "decisions": [
+                {
+                    "type": "approve",
+                }
+            ]
+        }
+    ),
+    config=config  # Same thread ID to resume the paused conversation
+)
+```
+:::
+
+:::js
+```typescript
+await agent.invoke(
+    new Command({
+        // Decisions are provided as a list, one per action under review.
+        // The order of decisions must match the order of actions
+        // listed in the `__interrupt__` request.
+        resume: {
+            decisions: [
+                {
+                    type: "approve",
+                }
+            ]
+        }
+    }),
+    config  // Same thread ID to resume the paused conversation
+);
+```
+:::
+</Tab>
+<Tab title="✏️ edit">
+    Use `edit` to modify the tool call before execution.
+    Provide the edited action with the new tool name and arguments.
+
+    :::python
+    ```python
+    agent.invoke(
+        Command(
+            # Decisions are provided as a list, one per action under review.
+            # The order of decisions must match the order of actions
+            # listed in the `__interrupt__` request.
+            resume={
+                "decisions": [
+                    {
+                        "type": "edit",
+                        # Edited action with tool name and args
+                        "edited_action": {
+                            # Tool name to call.
+                            # Will usually be the same as the original action.
+                            "name": "new_tool_name",
+                            # Arguments to pass to the tool.
+                            "args": {"key1": "new_value", "key2": "original_value"},
+                        }
+                    }
+                ]
+            }
+        ),
+        config=config  # Same thread ID to resume the paused conversation
+    )
+    ```
+    :::
+
+    :::js
+    ```typescript
+    await agent.invoke(
+        new Command({
+            // Decisions are provided as a list, one per action under review.
+            // The order of decisions must match the order of actions
+            // listed in the `__interrupt__` request.
+            resume: {
+                decisions: [
+                    {
+                        type: "edit",
+                        // Edited action with tool name and args
+                        editedAction: {
+                            // Tool name to call.
+                            // Will usually be the same as the original action.
+                            name: "new_tool_name",
+                            // Arguments to pass to the tool.
+                            args: { key1: "new_value", key2: "original_value" },
+                        }
+                    }
+                ]
+            }
+        }),
+        config  // Same thread ID to resume the paused conversation
+    );
+    ```
+    :::
+
+    <Tip>
+        When **editing** tool arguments, make changes conservatively. Significant modifications to the original arguments may cause the model to re-evaluate its approach and potentially execute the tool multiple times or take unexpected actions.
+    </Tip>
+
+</Tab>
+
+<Tab title="❌ reject">
+    Use `reject` to reject the tool call and provide feedback instead of execution.
+
+    :::python
+    ```python
+    agent.invoke(
+        Command(
+            # Decisions are provided as a list, one per action under review.
+            # The order of decisions must match the order of actions
+            # listed in the `__interrupt__` request.
+            resume={
+                "decisions": [
+                    {
+                        "type": "reject",
+                        # An explanation about why the action was rejected
+                        "message": "No, this is wrong because ..., instead do this ...",
+                    }
+                ]
+            }
+        ),
+        config=config  # Same thread ID to resume the paused conversation
+    )
+    ```
+    :::
+
+    :::js
+    ```typescript
+    await agent.invoke(
+        new Command({
+            // Decisions are provided as a list, one per action under review.
+            // The order of decisions must match the order of actions
+            // listed in the `__interrupt__` request.
+            resume: {
+                decisions: [
+                    {
+                        type: "reject",
+                        // An explanation about why the action was rejected
+                        message: "No, this is wrong because ..., instead do this ...",
+                    }
+                ]
+            }
+        }),
+        config  // Same thread ID to resume the paused conversation
+    );
+    ```
+    :::
+
+The `message` is added to the conversation as feedback to help the agent understand why the action was rejected and what it should do instead.
+
+---
+
+### Multiple decisions
+
+When multiple actions are under review, provide a decision for each action in the same order as they appear in the interrupt:
+
+:::python
+```python
+{
+    "decisions": [
+        {"type": "approve"},
+        {
+            "type": "edit",
+            "edited_action": {
+                "name": "tool_name",
+                "args": {"param": "new_value"}
+            }
+        },
+        {
+            "type": "reject",
+            "message": "This action is not allowed"
+        }
+    ]
+}
+```
+:::
+
+:::js
+```typescript
+{
+    decisions: [
+        { type: "approve" },
+        {
+            type: "edit",
+            editedAction: {
+                name: "tool_name",
+                args: { param: "new_value" }
+            }
+        },
+        {
+            type: "reject",
+            message: "This action is not allowed"
+        }
+    ]
+}
+```
+:::
+
+</Tab>
+</Tabs>
+
+
+## Execution lifecycle
+
+The middleware defines an `after_model` hook that runs after the model generates a response but before any tool calls are executed:
+
+1. The agent invokes the model to generate a response.
+2. The middleware inspects the response for tool calls.
+3. If any calls require human input, the middleware builds a `HITLRequest` with `action_requests` and `review_configs` and calls @[interrupt].
+4. The agent waits for human decisions.
+5. Based on the `HITLResponse` decisions, the middleware executes approved or edited calls, synthesizes @[ToolMessage]'s for rejected calls, and resumes execution.
+
+
+
+## Custom HITL logic
+
+For more specialized workflows, you can build custom HITL logic directly using the @[interrupt] primitive and [middleware](/oss/langchain/middleware) abstraction.
+
+Review the [execution lifecycle](#execution-lifecycle) above to understand how to integrate interrupts into the agent's operation.
+
diff --git a/notes_for_me.md b/notes_for_me.md
new file mode 100644
index 00000000..e9587f38
--- /dev/null
+++ b/notes_for_me.md
@@ -0,0 +1,10 @@
+new features
+* would be nice to check output of snippets too to make sure it matches, but reduce noise on things like nondeterministic output
+
+cleanup
+* reduce unnecessary configuration, added by claude at the beginning
+* go back and do realtime updates for tools (custom streaming) instead of the hacky conditional logic at the moment
+* we should do something much better in terms of sandboxing, right now using local bash -- we should be using middleware I think
+
+devx
+* we want more detailed updates from the subagents themselves