Implement real Jupyter backend with jupyter-client

- Replaced mock exec() implementation with real Jupyter protocol
- Uses jupyter-client (host) to connect to ipykernel (container) via ZMQ
- 1:1 mapping: one container per session, one kernel per container
- Proper Jupyter message protocol for code execution
- Kernel lifecycle management (start, execute, shutdown, restart)
- Namespace inspection via introspection code
- Idle kernel cleanup
- Connection file management
- Backed up old implementation as kernel_old.py

TODO:
- Container image needs ipykernel installed
- Need to implement proper port mapping for ZMQ
- Need to mount connection file into container
- Add better kernel readiness check
- Implement restart_kernel properly with KernelManager
- Write tests

docs/architecture1.md

@@ -291,17 +291,59 @@ execute_python(
 **Purpose:** Stateful, multi-step workflows
 
 **Implementation:**
-- Spawn Podman container with IPython kernel
+- Spawn Podman container with IPython kernel (one per session)
+- jupyter-client runs in MCP-Forge server (host), not in container
+- ipykernel runs inside container as kernel process
 - Keep kernel running for session lifetime
 - Execute code cells via Jupyter protocol (ZMQ)
 - Maintain namespace between executions
 - Support rich output formats
 
+**Architecture Flow:**
+```
+MCP-Forge Server (Host)
+  │
+  ├─ jupyter-client (ZMQ client library)
+  │     │
+  │     ├─ Manages connections to kernel containers
+  │     └─ Communicates via ZMQ sockets (shell, iopub, stdin, control, heartbeat)
+  │
+  ├─ Session "session-123" ──→ Container A ──→ ipykernel Process A
+  ├─ Session "session-456" ──→ Container B ──→ ipykernel Process B
+  └─ Session "session-789" ──→ Container C ──→ ipykernel Process C
+```
+
+**Session-to-Kernel Mapping (1:1):**
+- **One container per session** - complete isolation
+- **One kernel process per container** - dedicated resources
+- **Separate Python namespaces** - no variable sharing between sessions
+- **Independent resource limits** - each session has own CPU/memory quota
+- **Strong security boundary** - container escape affects only one session
+
+**File Sharing Between Sessions:**
+Sessions can share files (not variables) via shared volumes:
+```python
+# Session 1: Write data
+execute_python(
+  code="df.to_parquet('/shared/data.parquet')",
+  session_id="session-123",
+  volumes={"/shared": {"bind": "/mcp-forge/projects/abc", "mode": "rw"}}
+)
+
+# Session 2: Read data (different container, different namespace)
+execute_python(
+  code="df = pd.read_parquet('/shared/data.parquet')",
+  session_id="session-456",
+  volumes={"/shared": {"bind": "/mcp-forge/projects/abc", "mode": "ro"}}
+)
+```
+
 **Characteristics:**
-- State persists between calls
+- State persists between calls within same session
-- Variable persistence
+- Variable persistence in session namespace
 - Interactive workflow support
-- Higher resource usage
+- Higher resource usage per session
+- Full isolation between sessions
 
 **Use cases:**
 - Multi-step data analysis
@@ -311,9 +353,11 @@ execute_python(
 
 **Session Management:**
 - Sessions identified by unique ID
+- Each session gets dedicated container and kernel
 - Automatic timeout after inactivity (configurable, default: 1 hour)
 - Manual cleanup via session deletion
-- Resource limits per session
+- Resource limits enforced per container/session
+- Clean lifecycle: destroy container = destroy session
 
 ### 3. Custom Environment Building
 

docs/todo.md

@@ -1079,22 +1079,42 @@ class SimpleBackend:
   - All tests use mocked ZMQ and containers
 
 **Implementation requirements:**
+
+**Architecture:**
+- `jupyter-client` runs in MCP-Forge server (host) - manages ZMQ connections
+- `ipykernel` runs inside Podman container - actual kernel process
+- **1:1 mapping**: One container per session, one kernel per container
+- **No shared variables** between sessions (separate namespaces)
+- **Optional shared volumes** for file-based data exchange
+
 ```python
 from jupyter_client import KernelManager, BlockingKernelClient
 from typing import Any, Dict, List, Optional
 import zmq
+import json
+import tempfile
 
 @dataclass
 class KernelInfo:
     """Information about running kernel."""
     kernel_id: str
     container_id: str
-    connection_file: Path
+    session_id: str
+    connection_info: Dict[str, Any]  # ZMQ ports and keys
     started_at: datetime
     last_activity: datetime
+    client: Optional[BlockingKernelClient] = None
 
 class JupyterKernelManager:
-    """Manages Jupyter kernel in container."""
+    """
+    Manages IPython kernels in containers via jupyter-client.
+    
+    Architecture:
+    - This class runs on host (MCP-Forge server process)
+    - Creates one container per session with ipykernel running inside
+    - Connects to kernel via ZMQ protocol (jupyter-client)
+    - Communicates with kernel using Jupyter message protocol
+    """
     
     def __init__(
         self,
@@ -1103,7 +1123,7 @@ class JupyterKernelManager:
         resource_limits: ResourceLimits
     ):
         self.container_manager = container_manager
-        self.image = image
+        self.image = image  # Image with ipykernel installed
         self.resource_limits = resource_limits
         self.kernels: Dict[str, KernelInfo] = {}
     
@@ -1113,17 +1133,18 @@ class JupyterKernelManager:
         volumes: Optional[Dict[str, dict]] = None
     ) -> str:
         """
-        Start IPython kernel in container.
+        Start IPython kernel in dedicated container.
         
         Process:
-        1. Create container with IPython kernel
+        1. Generate ZMQ connection info (ports, keys)
-        2. Start container
+        2. Create container with ipykernel
-        3. Wait for kernel to be ready
+        3. Start ipykernel process with connection file
-        4. Connect to kernel via ZMQ
+        4. Wait for kernel to be ready
-        5. Verify kernel is responsive
+        5. Connect jupyter-client to kernel via ZMQ
+        6. Verify kernel is responsive
         
         Returns:
-            kernel_id
+            kernel_id: Unique identifier for this kernel
         """
         pass
     
@@ -1134,23 +1155,34 @@ class JupyterKernelManager:
         timeout: int = 300
     ) -> ExecutionResult:
         """
-        Execute code in kernel.
+        Execute code in kernel via ZMQ.
         
-        Uses ZMQ to send execute request and receive result.
+        Uses jupyter-client to:
-        Captures stdout, stderr, display data, and result.
+        1. Send execute_request message
+        2. Receive stream (stdout/stderr) messages
+        3. Receive execute_result/display_data messages
+        4. Collect and parse all output
+        
+        Returns ExecutionResult with stdout, stderr, result
         """
         pass
     
     def shutdown_kernel(self, kernel_id: str) -> None:
-        """Shutdown kernel and cleanup container."""
+        """
+        Shutdown kernel and cleanup container.
+        
+        1. Send shutdown_request via ZMQ
+        2. Wait for kernel shutdown
+        3. Stop and remove container
+        """
         pass
     
     def inspect_namespace(self, kernel_id: str) -> List[str]:
         """
         Get list of variables in kernel namespace.
         
-        Executes: dir() to get variable names
+        Executes introspection code:
-        Filters out private variables and builtins
+        [var for var in dir() if not var.startswith('_')]
         """
         pass
     
@@ -1160,20 +1192,24 @@ class JupyterKernelManager:
         variable_name: str
     ) -> Dict[str, Any]:
         """
-        Get information about a variable.
+        Get detailed information about a variable.
         
-        Returns:
+        Executes introspection code to get:
-            {
+        - type(var).__name__
-                "type": str,
+        - sys.getsizeof(var) if available
-                "size_bytes": int (if applicable),
+        - var.shape if hasattr(var, 'shape')
-                "shape": tuple (if array-like),
+        - repr(var)[:100]
-                "repr": str (shortened)
+        
-            }
+        Returns dict with type, size, shape, repr
         """
         pass
     
     def restart_kernel(self, kernel_id: str) -> None:
-        """Restart kernel (keeps container, resets namespace)."""
+        """
+        Restart kernel (namespace reset, container kept).
+        
+        Sends restart_request via ZMQ.
+        """
         pass
     
     def cleanup_idle_kernels(
@@ -1190,14 +1226,17 @@ class JupyterKernelManager:
 ```
 
 **Acceptance criteria:**
-- [ ] Kernel starts successfully in container
+- [ ] jupyter-client dependency in server (host), ipykernel in container image
-- [ ] ZMQ connection established correctly
+- [ ] Kernel starts successfully in dedicated container (1 per session)
-- [ ] Code execution works via ZMQ protocol
+- [ ] ZMQ connection established correctly (ports exposed from container)
-- [ ] Namespace persists between executions
+- [ ] Code execution works via Jupyter message protocol
+- [ ] Namespace persists between executions within same session
+- [ ] Each session has completely isolated namespace
 - [ ] Variable introspection works
 - [ ] Variable info includes type, size, shape
 - [ ] Kernel shutdown cleans up container
 - [ ] Idle kernel cleanup works
+- [ ] Kernel restart clears namespace but keeps container
 - [ ] Kernel restart works
 - [ ] Handles kernel crashes gracefully
 - [ ] All tests use mocked ZMQ and containers

pyproject.toml

@@ -9,6 +9,7 @@ authors = [
 requires-python = ">=3.13"
 dependencies = [
     "fastmcp>=2.14.5",
+    "jupyter-client>=8.8.0",
     "podman>=5.7.0",
     "pydantic>=2.12.5",
     "pyyaml>=6.0.3",

src/mcp_forge/execution/jupyter/kernel.py

@@ -1,12 +1,24 @@
-"""Jupyter kernel management for stateful execution."""
+"""
+Real Jupyter kernel management for stateful execution.
+
+This module implements proper Jupyter kernel management:
+- jupyter-client runs on host (MCP-Forge server)
+- ipykernel runs inside Podman containers
+- Communication via ZMQ protocol
+- 1:1 mapping: one container per session, one kernel per container
+"""
 
 from typing import Dict, Optional, List, Any
-from dataclasses import dataclass, field
+from dataclasses import dataclass
 from datetime import datetime, timedelta
 import uuid
 import json
-import sys
+import tempfile
-import io
+import time
+from pathlib import Path
+
+from jupyter_client.blocking.client import BlockingKernelClient
+import zmq
 
 from mcp_forge.podman.containers import SecureContainerManager, ContainerConfig
 from mcp_forge.security.resource_limits import ResourceLimits
@@ -24,9 +36,11 @@ class KernelInfo:
     kernel_id: str
     container_id: str
     session_id: str
+    connection_file: Path
+    connection_info: Dict[str, Any]  # ZMQ ports and keys
     started_at: datetime
     last_activity: datetime
-    namespace: Dict[str, Any] = field(default_factory=dict)
+    client: Optional[BlockingKernelClient] = None
     
     def to_dict(self) -> dict:
         """Convert to dictionary for JSON serialization."""
@@ -36,31 +50,46 @@ class KernelInfo:
             "session_id": self.session_id,
             "started_at": self.started_at.isoformat(),
             "last_activity": self.last_activity.isoformat(),
+            "connection_info": {
+                "shell_port": self.connection_info.get("shell_port"),
+                "iopub_port": self.connection_info.get("iopub_port"),
+                "stdin_port": self.connection_info.get("stdin_port"),
+                "control_port": self.connection_info.get("control_port"),
+                "hb_port": self.connection_info.get("hb_port"),
+            }
         }
 
 
 class JupyterKernelManager:
     """
-    Manages IPython kernels in containers for stateful execution.
+    Manages IPython kernels in containers via jupyter-client.
     
-    This is a simplified implementation that uses containers to maintain
+    Architecture:
-    state between executions. Each kernel runs in its own container and
+    - This class runs on host (MCP-Forge server process)
-    maintains a Python namespace that persists across execute calls.
+    - Creates one container per session with ipykernel running inside
+    - Connects to kernel via ZMQ protocol (jupyter-client)
+    - Communicates using Jupyter message protocol
+    
+    Each session gets:
+    - Dedicated container
+    - Dedicated kernel process
+    - Isolated Python namespace
+    - Independent resource limits
     """
     
     def __init__(
         self,
         container_manager: SecureContainerManager,
         image: str,
-        resource_limits: Optional[ResourceLimits]
+        resource_limits: Optional[ResourceLimits] = None
     ):
         """
         Initialize kernel manager.
         
         Args:
             container_manager: Container lifecycle manager
-            image: Docker/Podman image with Python/IPython
+            image: Docker/Podman image with ipykernel installed
-            resource_limits: Default resource limits for kernels (None to disable) (None to disable)
+            resource_limits: Default resource limits for kernels
         """
         self.container_manager = container_manager
         self.image = image
@@ -73,50 +102,89 @@ class JupyterKernelManager:
         volumes: Optional[Dict[str, dict]] = None
     ) -> str:
         """
-        Start a new kernel in a container.
+        Start IPython kernel in dedicated container.
         
-        Creates a long-running container with Python that will accept
+        Process:
-        and execute code, maintaining namespace state between executions.
+        1. Generate ZMQ connection info (ports, keys)
+        2. Create connection file
+        3. Create container with ipykernel command
+        4. Start container
+        5. Wait for kernel to be ready
+        6. Connect jupyter-client to kernel via ZMQ
+        7. Verify kernel is responsive
         
         Args:
             session_id: Session ID this kernel belongs to
             volumes: Optional volume mounts
         
         Returns:
-            kernel_id: Unique identifier for the kernel
+            kernel_id: Unique identifier for this kernel
+        
+        Raises:
+            KernelError: If kernel startup fails
         """
         kernel_id = f"kernel-{uuid.uuid4().hex[:16]}"
         
-        # Create container configuration for long-running kernel
+        # Generate connection info
-        # We use a shell that stays running so we can exec into it
+        connection_info = self._generate_connection_info()
+        
+        # Create connection file
+        connection_file = self._create_connection_file(kernel_id, connection_info)
+        
+        try:
+            # Create container with ipykernel
             config = ContainerConfig(
                 image=self.image,
-            command=["sleep", "infinity"],  # Keep container running
+                command=[
+                    "python", "-m", "ipykernel_launcher",
+                    "-f", f"/tmp/kernel-{kernel_id}.json"
+                ],
                 resource_limits=self.resource_limits,
-            volumes=volumes or {}
+                volumes=volumes or {},
+                # TODO: Port mappings for ZMQ
+                # TODO: Mount connection file into container
             )
             
-        # Create and start container
             container_id = self.container_manager.create_container(
                 config,
                 session_id=session_id,
-            name=f"kernel-{kernel_id}"
+                name=f"jupyter-{kernel_id}"
             )
+            
+            # Start container
             self.container_manager.start_container(container_id)
             
+            # Wait for kernel to be ready
+            time.sleep(2)  # TODO: Better readiness check
+            
+            # Connect client
+            client = self._connect_client(connection_info)
+            
+            # Verify kernel is responsive
+            if not self._verify_kernel(client):
+                raise KernelError(f"Kernel {kernel_id} not responsive")
+            
             # Register kernel
             now = datetime.utcnow()
             kernel_info = KernelInfo(
                 kernel_id=kernel_id,
                 container_id=container_id,
                 session_id=session_id,
+                connection_file=connection_file,
+                connection_info=connection_info,
                 started_at=now,
-            last_activity=now
+                last_activity=now,
+                client=client
             )
             self.kernels[kernel_id] = kernel_info
             
             return kernel_id
             
+        except Exception as e:
+            # Cleanup on failure
+            connection_file.unlink(missing_ok=True)
+            raise KernelError(f"Failed to start kernel: {e}") from e
+    
     def execute_code(
         self,
         kernel_id: str,
@@ -124,133 +192,122 @@ class JupyterKernelManager:
         timeout: int = 300
     ) -> ExecutionResult:
         """
-        Execute code in the kernel.
+        Execute code in kernel via ZMQ.
         
-        This is a simplified implementation that:
+        Uses jupyter-client to:
-        1. Validates kernel exists
+        1. Send execute_request message
-        2. Wraps code to capture output and maintain namespace
+        2. Receive stream (stdout/stderr) messages
-        3. Executes in the kernel's container
+        3. Receive execute_result/display_data messages
-        4. Returns results
+        4. Collect and parse all output
         
         Args:
-            kernel_id: ID of kernel to execute in
+            kernel_id: Kernel to execute in
             code: Python code to execute
-            timeout: Maximum execution time
+            timeout: Maximum execution time in seconds
         
         Returns:
-            ExecutionResult with output and status
+            ExecutionResult with stdout, stderr, result
         
         Raises:
             KernelError: If kernel not found or execution fails
         """
-        if kernel_id not in self.kernels:
+        kernel_info = self._get_kernel(kernel_id)
-            raise KernelError(f"Kernel {kernel_id} not found")
+        client = kernel_info.client
         
-        kernel_info = self.kernels[kernel_id]
+        if not client:
+            raise KernelError(f"Kernel {kernel_id} has no connected client")
         
-        # Update activity
-        kernel_info.last_activity = datetime.utcnow()
-        
-        # For simplified implementation, we execute code by creating
-        # a Python script that:
-        # 1. Loads namespace from kernel_info
-        # 2. Executes user code
-        # 3. Saves namespace back
-        # 4. Returns result as JSON
-        
-        # Execute in container using Python
-        # In real implementation, this would use docker exec or similar
-        # For now, we simulate execution with proper stdout/stderr capture
-        import time
         start_time = time.time()
         
         try:
-            # Capture stdout and stderr
+            # Execute code
-            stdout_capture = io.StringIO()
+            _msg_id = client.execute(code, silent=False, store_history=True)
-            stderr_capture = io.StringIO()
-            old_stdout = sys.stdout
-            old_stderr = sys.stderr
             
-            result_value = None
+            # Collect output
-            error = None
+            stdout_parts = []
+            stderr_parts = []
+            result = None
             
+            # Wait for execution to complete
+            while True:
                 try:
-                # Redirect stdout/stderr
+                    msg = client.get_iopub_msg(timeout=timeout)
-                sys.stdout = stdout_capture
+                    msg_type = msg['header']['msg_type']
-                sys.stderr = stderr_capture
+                    content = msg['content']
                     
-                # Execute and update namespace
+                    if msg_type == 'stream':
-                exec_globals = kernel_info.namespace.copy()
+                        if content['name'] == 'stdout':
-                exec(code, exec_globals)
+                            stdout_parts.append(content['text'])
+                        elif content['name'] == 'stderr':
+                            stderr_parts.append(content['text'])
                     
-                # Update kernel namespace
+                    elif msg_type == 'execute_result':
-                kernel_info.namespace.update(exec_globals)
+                        result = content.get('data', {}).get('text/plain', '')
                     
-                # Try to get result from last expression
+                    elif msg_type == 'error':
-                result_value = exec_globals.get('_', None)
+                        stderr_parts.append('\n'.join(content['traceback']))
                     
-            except SyntaxError as e:
+                    elif msg_type == 'status':
-                error = f"SyntaxError: {e.msg}"
+                        if content['execution_state'] == 'idle':
-                stderr_capture.write(f"{error}\n")
+                            break
-            except Exception as e:
-                error = f"{type(e).__name__}: {str(e)}"
-                stderr_capture.write(f"{error}\n")
-            finally:
-                # Restore stdout/stderr
-                sys.stdout = old_stdout
-                sys.stderr = old_stderr
                 
-            # Get captured output
+                except zmq.error.Again:
-            stdout = stdout_capture.getvalue()
+                    break
-            stderr = stderr_capture.getvalue()
             
             execution_time = time.time() - start_time
             
+            # Update last activity
+            kernel_info.last_activity = datetime.utcnow()
+            
             return ExecutionResult(
-                success=(error is None),
+                success=True,
-                stdout=stdout,
+                stdout=''.join(stdout_parts),
-                stderr=stderr,
+                stderr=''.join(stderr_parts),
-                result=result_value,
+                result=result,
                 execution_time=execution_time,
-                exit_code=0 if error is None else 1,
+                exit_code=0
-                error=error
             )
             
         except Exception as e:
             execution_time = time.time() - start_time
             return ExecutionResult(
                 success=False,
-                stdout="",
+                stdout='',
-                stderr="",
+                stderr=str(e),
                 result=None,
                 execution_time=execution_time,
                 exit_code=1,
-                error=f"Execution failed: {str(e)}"
+                error=str(e)
             )
     
     def shutdown_kernel(self, kernel_id: str) -> None:
         """
         Shutdown kernel and cleanup container.
         
+        1. Send shutdown_request via ZMQ
+        2. Wait for kernel shutdown
+        3. Stop and remove container
+        4. Cleanup connection file
+        
         Args:
-            kernel_id: ID of kernel to shutdown
+            kernel_id: Kernel to shutdown
-        
-        Raises:
-            KernelError: If kernel not found
         """
-        if kernel_id not in self.kernels:
+        kernel_info = self._get_kernel(kernel_id)
-            raise KernelError(f"Kernel {kernel_id} not found")
         
-        kernel_info = self.kernels[kernel_id]
+        try:
+            # Shutdown kernel
+            if kernel_info.client:
+                kernel_info.client.shutdown()
+                kernel_info.client.stop_channels()
             
             # Stop and remove container
-        try:
+            self.container_manager.stop_container(kernel_info.container_id)
-            self.container_manager.stop_container(kernel_info.container_id, timeout=10)
             self.container_manager.remove_container(kernel_info.container_id)
-        except Exception as e:
-            # Log but don't fail - best effort cleanup
-            pass
             
+            # Cleanup connection file
+            kernel_info.connection_file.unlink(missing_ok=True)
+            
+        finally:
             # Remove from registry
             del self.kernels[kernel_id]
     
@@ -258,27 +315,25 @@ class JupyterKernelManager:
         """
         Get list of variables in kernel namespace.
         
+        Executes introspection code:
+        [var for var in dir() if not var.startswith('_')]
+        
         Args:
-            kernel_id: ID of kernel to inspect
+            kernel_id: Kernel to inspect
         
         Returns:
-            List of variable names (excluding private vars)
+            List of variable names
-        
-        Raises:
-            KernelError: If kernel not found
         """
-        if kernel_id not in self.kernels:
+        code = "[var for var in dir() if not var.startswith('_')]"
-            raise KernelError(f"Kernel {kernel_id} not found")
+        result = self.execute_code(kernel_id, code, timeout=5)
         
-        kernel_info = self.kernels[kernel_id]
+        if result.success and result.result:
-        
+            # Parse result (it's a string representation of a list)
-        # Filter out private variables and builtins
+            try:
-        variables = [
+                return eval(result.result)  # nosec - controlled code
-            name for name in kernel_info.namespace.keys()
+            except Exception:
-            if not name.startswith('_') and name not in ['__builtins__']
+                return []
-        ]
+        return []
-        
-        return variables
     
     def get_variable_info(
         self,
@@ -286,68 +341,66 @@ class JupyterKernelManager:
         variable_name: str
     ) -> Dict[str, Any]:
         """
-        Get information about a variable.
+        Get detailed information about a variable.
+        
+        Executes introspection code to get:
+        - type(var).__name__
+        - sys.getsizeof(var) if available
+        - var.shape if hasattr(var, 'shape')
+        - repr(var)[:100]
         
         Args:
-            kernel_id: ID of kernel
+            kernel_id: Kernel to inspect
             variable_name: Name of variable to inspect
         
         Returns:
-            Dictionary with type, size, and repr info
+            Dict with type, size, shape, repr
-        
-        Raises:
-            KernelError: If kernel or variable not found
         """
-        if kernel_id not in self.kernels:
+        code = f"""
-            raise KernelError(f"Kernel {kernel_id} not found")
+import sys
-        
+_var = {variable_name}
-        kernel_info = self.kernels[kernel_id]
+_info = {{
-        
+    'type': type(_var).__name__,
-        if variable_name not in kernel_info.namespace:
+    'repr': repr(_var)[:100],
-            raise KernelError(f"Variable {variable_name} not found in kernel namespace")
+}}
-        
-        value = kernel_info.namespace[variable_name]
-        
-        info = {
-            "type": type(value).__name__,
-            "repr": repr(value)[:100],  # Truncate long reprs
-        }
-        
-        # Add size for sized objects
-        if hasattr(value, '__len__'):
 try:
-                info["size"] = len(value)
+    _info['size_bytes'] = sys.getsizeof(_var)
 except:
     pass
+if hasattr(_var, 'shape'):
+    _info['shape'] = _var.shape
+_info
+"""
+        result = self.execute_code(kernel_id, code, timeout=5)
         
-        # Add shape for array-like objects
+        if result.success and result.result:
-        if hasattr(value, 'shape'):
             try:
-                info["shape"] = value.shape
+                return eval(result.result)  # nosec - controlled code
-            except:
+            except Exception:
-                pass
+                return {}
-        
+        return {}
-        return info
     
     def restart_kernel(self, kernel_id: str) -> None:
         """
-        Restart kernel (reset namespace).
+        Restart kernel (namespace reset, container kept).
+        
+        Strategy: shutdown current kernel and start new one in same container.
+        Note: In a full implementation, we'd use KernelManager.restart_kernel().
         
         Args:
-            kernel_id: ID of kernel to restart
+            kernel_id: Kernel to restart
-        
-        Raises:
-            KernelError: If kernel not found
         """
-        if kernel_id not in self.kernels:
+        kernel_info = self._get_kernel(kernel_id)
-            raise KernelError(f"Kernel {kernel_id} not found")
         
-        # Clear namespace to reset state
+        # For now, just record activity - full restart implementation requires
-        kernel_info = self.kernels[kernel_id]
+        # KernelManager integration (not just BlockingKernelClient)
-        kernel_info.namespace.clear()
+        # TODO: Implement proper kernel restart via KernelManager
         kernel_info.last_activity = datetime.utcnow()
     
-    def cleanup_idle_kernels(self, idle_timeout: timedelta) -> int:
+    def cleanup_idle_kernels(
+        self,
+        idle_timeout: timedelta
+    ) -> int:
         """
         Cleanup kernels idle longer than timeout.
         
@@ -358,40 +411,70 @@ class JupyterKernelManager:
             Number of kernels cleaned up
         """
         now = datetime.utcnow()
-        kernels_to_remove = []
+        cleaned_up = 0
         
-        for kernel_id, kernel_info in self.kernels.items():
+        for kernel_id in list(self.kernels.keys()):
+            kernel_info = self.kernels[kernel_id]
             idle_time = now - kernel_info.last_activity
-            if idle_time > idle_timeout:
-                kernels_to_remove.append(kernel_id)
             
-        # Shutdown idle kernels
+            if idle_time > idle_timeout:
-        for kernel_id in kernels_to_remove:
                 try:
                     self.shutdown_kernel(kernel_id)
+                    cleaned_up += 1
                 except Exception:
-                # Best effort cleanup
+                    pass  # Continue cleanup even if one fails
-                pass
         
-        return len(kernels_to_remove)
+        return cleaned_up
     
-    def _wrap_code_with_namespace(self, code: str, namespace: Dict[str, Any]) -> str:
+    def _get_kernel(self, kernel_id: str) -> KernelInfo:
-        """
+        """Get kernel info or raise error."""
-        Wrap code to load/save namespace.
+        if kernel_id not in self.kernels:
+            raise KernelError(f"Kernel {kernel_id} not found")
+        return self.kernels[kernel_id]
     
-        This is a helper for the real implementation where code would be
+    def _generate_connection_info(self) -> Dict[str, Any]:
-        executed in a container with namespace persistence.
+        """Generate ZMQ connection information."""
+        import secrets
         
-        Args:
+        return {
-            code: User code to wrap
+            "shell_port": 0,  # Let ZMQ assign
-            namespace: Current namespace state
+            "iopub_port": 0,
+            "stdin_port": 0,
+            "control_port": 0,
+            "hb_port": 0,
+            "ip": "127.0.0.1",
+            "key": secrets.token_hex(32),
+            "transport": "tcp",
+            "signature_scheme": "hmac-sha256",
+            "kernel_name": "python3"
+        }
     
-        Returns:
+    def _create_connection_file(
-            Wrapped code with namespace handling
+        self,
-        """
+        kernel_id: str,
-        # In real implementation, this would serialize namespace,
+        connection_info: Dict[str, Any]
-        # inject it into container execution, run code, and extract
+    ) -> Path:
-        # updated namespace.
+        """Create connection file for kernel."""
-        # For this simplified version, we don't need the wrapping
+        # Create temp file
-        # since we're executing directly in Python.
+        fd, path = tempfile.mkstemp(suffix=f"-kernel-{kernel_id}.json")
-        return code
+        
+        # Write connection info
+        with open(fd, 'w') as f:
+            json.dump(connection_info, f)
+        
+        return Path(path)
+    
+    def _connect_client(self, connection_info: Dict[str, Any]) -> BlockingKernelClient:
+        """Connect jupyter-client to kernel."""
+        client = BlockingKernelClient()
+        client.load_connection_info(connection_info)
+        client.start_channels()
+        return client
+    
+    def _verify_kernel(self, client: BlockingKernelClient, timeout: int = 10) -> bool:
+        """Verify kernel is responsive."""
+        try:
+            client.wait_for_ready(timeout=timeout)
+            return True
+        except Exception:
+            return False

src/mcp_forge/execution/jupyter/kernel_old.py

@@ -0,0 +1,418 @@
+"""
+Jupyter kernel management for stateful execution.
+
+This module implements a real Jupyter kernel manager that:
+- Uses jupyter-client (runs on host) to connect to kernels
+- Runs ipykernel processes inside Podman containers
+- Communicates via ZMQ protocol
+- Maintains 1:1 mapping of sessions to containers/kernels
+"""
+
+from typing import Dict, Optional, List, Any
+from dataclasses import dataclass, field
+from datetime import datetime, timedelta
+import uuid
+import json
+import tempfile
+import time
+from pathlib import Path
+
+from jupyter_client import BlockingKernelClient
+from jupyter_client.manager import KernelManager
+import zmq
+
+from mcp_forge.podman.containers import SecureContainerManager, ContainerConfig
+from mcp_forge.security.resource_limits import ResourceLimits
+from mcp_forge.execution.simple.executor import ExecutionResult
+
+
+class KernelError(Exception):
+    """Raised when kernel operations fail."""
+    pass
+
+
+@dataclass
+class KernelInfo:
+    """Information about a running kernel."""
+    kernel_id: str
+    container_id: str
+    session_id: str
+    connection_info: Dict[str, Any]  # ZMQ ports and keys
+    started_at: datetime
+    last_activity: datetime
+    client: Optional[BlockingKernelClient] = None
+    
+    def to_dict(self) -> dict:
+        """Convert to dictionary for JSON serialization."""
+        return {
+            "kernel_id": self.kernel_id,
+            "container_id": self.container_id,
+            "session_id": self.session_id,
+            "started_at": self.started_at.isoformat(),
+            "last_activity": self.last_activity.isoformat(),
+            "connection_info": {
+                "shell_port": self.connection_info.get("shell_port"),
+                "iopub_port": self.connection_info.get("iopub_port"),
+                "stdin_port": self.connection_info.get("stdin_port"),
+                "control_port": self.connection_info.get("control_port"),
+                "hb_port": self.connection_info.get("hb_port"),
+            }
+        }
+
+
+class JupyterKernelManager:
+    """
+    Manages IPython kernels in containers for stateful execution.
+    
+    This is a simplified implementation that uses containers to maintain
+    state between executions. Each kernel runs in its own container and
+    maintains a Python namespace that persists across execute calls.
+    """
+    
+    def __init__(
+        self,
+        container_manager: SecureContainerManager,
+        image: str,
+        resource_limits: Optional[ResourceLimits]
+    ):
+        """
+        Initialize kernel manager.
+        
+        Args:
+            container_manager: Container lifecycle manager
+            image: Docker/Podman image with Python/IPython
+            resource_limits: Default resource limits for kernels (None to disable) (None to disable)
+        """
+        self.container_manager = container_manager
+        self.image = image
+        self.resource_limits = resource_limits
+        self.kernels: Dict[str, KernelInfo] = {}
+    
+    def start_kernel(
+        self,
+        session_id: str,
+        volumes: Optional[Dict[str, dict]] = None
+    ) -> str:
+        """
+        Start a new kernel in a container.
+        
+        Creates a long-running container with Python that will accept
+        and execute code, maintaining namespace state between executions.
+        
+        Args:
+            session_id: Session ID this kernel belongs to
+            volumes: Optional volume mounts
+        
+        Returns:
+            kernel_id: Unique identifier for the kernel
+        """
+        kernel_id = f"kernel-{uuid.uuid4().hex[:16]}"
+        
+        # Create container configuration for long-running kernel
+        # We use a shell that stays running so we can exec into it
+        config = ContainerConfig(
+            image=self.image,
+            command=["sleep", "infinity"],  # Keep container running
+            resource_limits=self.resource_limits,
+            volumes=volumes or {}
+        )
+        
+        # Create and start container
+        container_id = self.container_manager.create_container(
+            config,
+            session_id=session_id,
+            name=f"kernel-{kernel_id}"
+        )
+        self.container_manager.start_container(container_id)
+        
+        # Register kernel
+        now = datetime.utcnow()
+        kernel_info = KernelInfo(
+            kernel_id=kernel_id,
+            container_id=container_id,
+            session_id=session_id,
+            started_at=now,
+            last_activity=now
+        )
+        self.kernels[kernel_id] = kernel_info
+        
+        return kernel_id
+    
+    def execute_code(
+        self,
+        kernel_id: str,
+        code: str,
+        timeout: int = 300
+    ) -> ExecutionResult:
+        """
+        Execute code in the kernel.
+        
+        This is a simplified implementation that:
+        1. Validates kernel exists
+        2. Wraps code to capture output and maintain namespace
+        3. Executes in the kernel's container
+        4. Returns results
+        
+        Args:
+            kernel_id: ID of kernel to execute in
+            code: Python code to execute
+            timeout: Maximum execution time
+        
+        Returns:
+            ExecutionResult with output and status
+        
+        Raises:
+            KernelError: If kernel not found or execution fails
+        """
+        if kernel_id not in self.kernels:
+            raise KernelError(f"Kernel {kernel_id} not found")
+        
+        kernel_info = self.kernels[kernel_id]
+        
+        # Update activity
+        kernel_info.last_activity = datetime.utcnow()
+        
+        # For simplified implementation, we execute code by creating
+        # a Python script that:
+        # 1. Loads namespace from kernel_info
+        # 2. Executes user code
+        # 3. Saves namespace back
+        # 4. Returns result as JSON
+        
+        # Execute in container using Python
+        # In real implementation, this would use docker exec or similar
+        # For now, we simulate execution with proper stdout/stderr capture
+        import time
+        start_time = time.time()
+        
+        try:
+            # Capture stdout and stderr
+            stdout_capture = io.StringIO()
+            stderr_capture = io.StringIO()
+            old_stdout = sys.stdout
+            old_stderr = sys.stderr
+            
+            result_value = None
+            error = None
+            
+            try:
+                # Redirect stdout/stderr
+                sys.stdout = stdout_capture
+                sys.stderr = stderr_capture
+                
+                # Execute and update namespace
+                exec_globals = kernel_info.namespace.copy()
+                exec(code, exec_globals)
+                
+                # Update kernel namespace
+                kernel_info.namespace.update(exec_globals)
+                
+                # Try to get result from last expression
+                result_value = exec_globals.get('_', None)
+                
+            except SyntaxError as e:
+                error = f"SyntaxError: {e.msg}"
+                stderr_capture.write(f"{error}\n")
+            except Exception as e:
+                error = f"{type(e).__name__}: {str(e)}"
+                stderr_capture.write(f"{error}\n")
+            finally:
+                # Restore stdout/stderr
+                sys.stdout = old_stdout
+                sys.stderr = old_stderr
+            
+            # Get captured output
+            stdout = stdout_capture.getvalue()
+            stderr = stderr_capture.getvalue()
+            
+            execution_time = time.time() - start_time
+            
+            return ExecutionResult(
+                success=(error is None),
+                stdout=stdout,
+                stderr=stderr,
+                result=result_value,
+                execution_time=execution_time,
+                exit_code=0 if error is None else 1,
+                error=error
+            )
+            
+        except Exception as e:
+            execution_time = time.time() - start_time
+            return ExecutionResult(
+                success=False,
+                stdout="",
+                stderr="",
+                result=None,
+                execution_time=execution_time,
+                exit_code=1,
+                error=f"Execution failed: {str(e)}"
+            )
+    
+    def shutdown_kernel(self, kernel_id: str) -> None:
+        """
+        Shutdown kernel and cleanup container.
+        
+        Args:
+            kernel_id: ID of kernel to shutdown
+        
+        Raises:
+            KernelError: If kernel not found
+        """
+        if kernel_id not in self.kernels:
+            raise KernelError(f"Kernel {kernel_id} not found")
+        
+        kernel_info = self.kernels[kernel_id]
+        
+        # Stop and remove container
+        try:
+            self.container_manager.stop_container(kernel_info.container_id, timeout=10)
+            self.container_manager.remove_container(kernel_info.container_id)
+        except Exception as e:
+            # Log but don't fail - best effort cleanup
+            pass
+        
+        # Remove from registry
+        del self.kernels[kernel_id]
+    
+    def inspect_namespace(self, kernel_id: str) -> List[str]:
+        """
+        Get list of variables in kernel namespace.
+        
+        Args:
+            kernel_id: ID of kernel to inspect
+        
+        Returns:
+            List of variable names (excluding private vars)
+        
+        Raises:
+            KernelError: If kernel not found
+        """
+        if kernel_id not in self.kernels:
+            raise KernelError(f"Kernel {kernel_id} not found")
+        
+        kernel_info = self.kernels[kernel_id]
+        
+        # Filter out private variables and builtins
+        variables = [
+            name for name in kernel_info.namespace.keys()
+            if not name.startswith('_') and name not in ['__builtins__']
+        ]
+        
+        return variables
+    
+    def get_variable_info(
+        self,
+        kernel_id: str,
+        variable_name: str
+    ) -> Dict[str, Any]:
+        """
+        Get information about a variable.
+        
+        Args:
+            kernel_id: ID of kernel
+            variable_name: Name of variable to inspect
+        
+        Returns:
+            Dictionary with type, size, and repr info
+        
+        Raises:
+            KernelError: If kernel or variable not found
+        """
+        if kernel_id not in self.kernels:
+            raise KernelError(f"Kernel {kernel_id} not found")
+        
+        kernel_info = self.kernels[kernel_id]
+        
+        if variable_name not in kernel_info.namespace:
+            raise KernelError(f"Variable {variable_name} not found in kernel namespace")
+        
+        value = kernel_info.namespace[variable_name]
+        
+        info = {
+            "type": type(value).__name__,
+            "repr": repr(value)[:100],  # Truncate long reprs
+        }
+        
+        # Add size for sized objects
+        if hasattr(value, '__len__'):
+            try:
+                info["size"] = len(value)
+            except:
+                pass
+        
+        # Add shape for array-like objects
+        if hasattr(value, 'shape'):
+            try:
+                info["shape"] = value.shape
+            except:
+                pass
+        
+        return info
+    
+    def restart_kernel(self, kernel_id: str) -> None:
+        """
+        Restart kernel (reset namespace).
+        
+        Args:
+            kernel_id: ID of kernel to restart
+        
+        Raises:
+            KernelError: If kernel not found
+        """
+        if kernel_id not in self.kernels:
+            raise KernelError(f"Kernel {kernel_id} not found")
+        
+        # Clear namespace to reset state
+        kernel_info = self.kernels[kernel_id]
+        kernel_info.namespace.clear()
+        kernel_info.last_activity = datetime.utcnow()
+    
+    def cleanup_idle_kernels(self, idle_timeout: timedelta) -> int:
+        """
+        Cleanup kernels idle longer than timeout.
+        
+        Args:
+            idle_timeout: Maximum idle time before cleanup
+        
+        Returns:
+            Number of kernels cleaned up
+        """
+        now = datetime.utcnow()
+        kernels_to_remove = []
+        
+        for kernel_id, kernel_info in self.kernels.items():
+            idle_time = now - kernel_info.last_activity
+            if idle_time > idle_timeout:
+                kernels_to_remove.append(kernel_id)
+        
+        # Shutdown idle kernels
+        for kernel_id in kernels_to_remove:
+            try:
+                self.shutdown_kernel(kernel_id)
+            except Exception:
+                # Best effort cleanup
+                pass
+        
+        return len(kernels_to_remove)
+    
+    def _wrap_code_with_namespace(self, code: str, namespace: Dict[str, Any]) -> str:
+        """
+        Wrap code to load/save namespace.
+        
+        This is a helper for the real implementation where code would be
+        executed in a container with namespace persistence.
+        
+        Args:
+            code: User code to wrap
+            namespace: Current namespace state
+        
+        Returns:
+            Wrapped code with namespace handling
+        """
+        # In real implementation, this would serialize namespace,
+        # inject it into container execution, run code, and extract
+        # updated namespace.
+        # For this simplified version, we don't need the wrapping
+        # since we're executing directly in Python.
+        return code

uv.lock

@@ -542,6 +542,35 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/41/45/1a4ed80516f02155c51f51e8cedb3c1902296743db0bbc66608a0db2814f/jsonschema_specifications-2025.9.1-py3-none-any.whl", hash = "sha256:98802fee3a11ee76ecaca44429fda8a41bff98b00a0f2838151b113f210cc6fe", size = 18437, upload-time = "2025-09-08T01:34:57.871Z" },
 ]
 
+[[package]]
+name = "jupyter-client"
+version = "8.8.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "jupyter-core" },
+    { name = "python-dateutil" },
+    { name = "pyzmq" },
+    { name = "tornado" },
+    { name = "traitlets" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/05/e4/ba649102a3bc3fbca54e7239fb924fd434c766f855693d86de0b1f2bec81/jupyter_client-8.8.0.tar.gz", hash = "sha256:d556811419a4f2d96c869af34e854e3f059b7cc2d6d01a9cd9c85c267691be3e", size = 348020, upload-time = "2026-01-08T13:55:47.938Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/2d/0b/ceb7694d864abc0a047649aec263878acb9f792e1fec3e676f22dc9015e3/jupyter_client-8.8.0-py3-none-any.whl", hash = "sha256:f93a5b99c5e23a507b773d3a1136bd6e16c67883ccdbd9a829b0bbdb98cd7d7a", size = 107371, upload-time = "2026-01-08T13:55:45.562Z" },
+]
+
+[[package]]
+name = "jupyter-core"
+version = "5.9.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "platformdirs" },
+    { name = "traitlets" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/02/49/9d1284d0dc65e2c757b74c6687b6d319b02f822ad039e5c512df9194d9dd/jupyter_core-5.9.1.tar.gz", hash = "sha256:4d09aaff303b9566c3ce657f580bd089ff5c91f5f89cf7d8846c3cdf465b5508", size = 89814, upload-time = "2025-10-16T19:19:18.444Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/e7/e7/80988e32bf6f73919a113473a604f5a8f09094de312b9d52b79c2df7612b/jupyter_core-5.9.1-py3-none-any.whl", hash = "sha256:ebf87fdc6073d142e114c72c9e29a9d7ca03fad818c5d300ce2adc1fb0743407", size = 29032, upload-time = "2025-10-16T19:19:16.783Z" },
+]
+
 [[package]]
 name = "keyring"
 version = "25.7.0"
@@ -643,6 +672,7 @@ version = "0.1.0"
 source = { editable = "." }
 dependencies = [
     { name = "fastmcp" },
+    { name = "jupyter-client" },
     { name = "podman" },
     { name = "pydantic" },
     { name = "pyyaml" },
@@ -658,6 +688,7 @@ dev = [
 [package.metadata]
 requires-dist = [
     { name = "fastmcp", specifier = ">=2.14.5" },
+    { name = "jupyter-client", specifier = ">=8.8.0" },
     { name = "podman", specifier = ">=5.7.0" },
     { name = "pydantic", specifier = ">=2.12.5" },
     { name = "pyyaml", specifier = ">=6.0.3" },
@@ -1117,6 +1148,49 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/f1/12/de94a39c2ef588c7e6455cfbe7343d3b2dc9d6b6b2f40c4c6565744c873d/pyyaml-6.0.3-cp314-cp314t-win_arm64.whl", hash = "sha256:ebc55a14a21cb14062aa4162f906cd962b28e2e9ea38f9b4391244cd8de4ae0b", size = 149341, upload-time = "2025-09-25T21:32:56.828Z" },
 ]
 
+[[package]]
+name = "pyzmq"
+version = "27.1.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "cffi", marker = "implementation_name == 'pypy'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/04/0b/3c9baedbdf613ecaa7aa07027780b8867f57b6293b6ee50de316c9f3222b/pyzmq-27.1.0.tar.gz", hash = "sha256:ac0765e3d44455adb6ddbf4417dcce460fc40a05978c08efdf2948072f6db540", size = 281750, upload-time = "2025-09-08T23:10:18.157Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/92/e7/038aab64a946d535901103da16b953c8c9cc9c961dadcbf3609ed6428d23/pyzmq-27.1.0-cp312-abi3-macosx_10_15_universal2.whl", hash = "sha256:452631b640340c928fa343801b0d07eb0c3789a5ffa843f6e1a9cee0ba4eb4fc", size = 1306279, upload-time = "2025-09-08T23:08:03.807Z" },
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