gns3-server/docs/gns3-copilot/template-based-configuration-roadmap.md

Template-Based Configuration with HITL - Future Roadmap

Status: 💡 Proposed Target Version: Next Release Last Updated: 2026-03-20

Overview

This document outlines the plan for implementing a Jinja2-based template system with Human-in-the-Loop (HITL) confirmations for network device configuration in GNS3 AI Copilot.

Motivation

The current implementation requires AI to generate complete configuration commands for every device, which:

• Consumes excessive tokens: Each device configuration is generated independently (~150 tokens/device × 10 devices = 1500 tokens)
• Lacks user control: Configurations are executed immediately without human review
• No reusability: Similar configurations must be regenerated from scratch
• Higher error risk: Direct execution without preview or confirmation

Proposed Solution

Implement a three-step HITL workflow using Jinja2 templates:

1. AI generates template → Human reviews and confirms
2. AI generates parameters → Human reviews and confirms
3. Local rendering and execution → Results displayed

Expected Token Savings: 70-80% reduction for multi-device configurations

---

Architecture Design

Workflow Diagram

User Request: "Configure OSPF on all routers"
                    ↓
┌─────────────────────────────────────────────────────────────┐
│ Step 1: AI Generates Jinja2 Template                         │
│                                                              │
│ Output:                                                      │
│ {                                                            │
│   "template_content": "router ospf {{ pid }}\n...",         │
│   "description": "OSPF basic configuration",                │
│   "params_schema": {                                         │
│     "process_id": "int - OSPF process ID",                  │
│     "networks": "List[Dict] - network list",                │
│     "area": "str - area ID"                                 │
│   }                                                          │
│ }                                                            │
└─────────────────────────────────────────────────────────────┘
                    ↓
┌─────────────────────────────────────────────────────────────┐
│ 🔵 HITL Checkpoint 1: Template Review                       │
│                                                              │
│ User sees:                                                  │
│ - Template content (Jinja2 syntax)                          │
│ - Parameter schema                                          │
│ - Example rendered output                                   │
│                                                              │
│ Options: [✓ Confirm] [✏️ Modify] [❌ Cancel]                 │
└─────────────────────────────────────────────────────────────┘
                    ↓
┌─────────────────────────────────────────────────────────────┐
│ Step 2: AI Generates Parameters                             │
│                                                              │
│ Output:                                                      │
│ {                                                            │
│   "project_id": "uuid-xxx",                                  │
│   "device_params": [                                         │
│     {                                                        │
│       "device_name": "R1",                                   │
│       "process_id": 1,                                       │
│       "networks": [{"ip": "192.168.1.0", "mask": "0.0.0.255"}], │
│       "area": "0"                                            │
│     },                                                       │
│     ... // More devices                                      │
│   ]                                                          │
│ }                                                            │
└─────────────────────────────────────────────────────────────┘
                    ↓
┌─────────────────────────────────────────────────────────────┐
│ 🔵 HITL Checkpoint 2: Parameter Review                      │
│                                                              │
│ User sees:                                                  │
│ - Parameter preview per device                              │
│ - Rendered configuration commands                           │
│ - Summary of changes                                         │
│                                                              │
│ Options: [✓ Execute] [✏️ Modify] [👁️ Preview] [❌ Cancel]    │
└─────────────────────────────────────────────────────────────┘
                    ↓
┌─────────────────────────────────────────────────────────────┐
│ Step 3: Local Rendering & Execution                         │
│                                                              │
│ Process:                                                     │
│ 1. Render template with parameters (0 tokens)               │
│ 2. Call existing ExecuteMultipleDeviceConfigCommands        │
│ 3. Return execution results                                  │
└─────────────────────────────────────────────────────────────┘

Token Consumption Comparison

Scenario: Configure OSPF on 10 Cisco Routers

Approach	Token Usage	Breakdown
Current Method	~1500 tokens	150 tokens/device × 10 devices
Template Method	~400 tokens	Template: 150 + Parameters: 250
Savings	73%	1100 tokens saved

Scenario: Configure VLANs on 20 Switches

Approach	Token Usage	Breakdown
Current Method	~1600 tokens	80 tokens/switch × 20 switches
Template Method	~400 tokens	Template: 100 + Parameters: 300
Savings	75%	1200 tokens saved

🔥 Scenario: Large-Scale Topology - 500+ Routers

This is where the template-based approach truly shines for rapid environment provisioning.

Approach	Token Usage	Execution Time	Breakdown
Current Method (AI)	~75,000 tokens	~25 minutes	150 tokens/device × 500 devices, serial execution
Template + AI	~5,000 tokens	~10 minutes	Template once + AI generates params, but slow
Template + Rules (Direct)	~400 tokens	~3 minutes	Template once + rule engine (0 tokens) + parallel execution
Savings	99.5%	88%	Game-changing for large deployments

Key Insight: For environments with hundreds or thousands of nodes, the direct execution mode (skipping AI) becomes critical for rapid topology preparation.

---

Core Components

1. New LangChain Tools

Tool 1: GenerateConfigTemplate

class GenerateConfigTemplate(BaseTool):
    """
    Generates Jinja2 configuration templates for human review.

    This tool ONLY generates templates. No configuration is executed.

    Input:
    {
        "project_id": "project-uuid",
        "device_type": "cisco_ios | huawei_vrp | ...",
        "requirement": "user requirement description"
    }

    Output:
    {
        "template_content": "jinja2 template string",
        "template_description": "human-readable description",
        "params_schema": {
            "param_name": "type - description"
        },
        "rendered_example": "example output with sample data"
    }
    """

    name = "generate_config_template"
    description = "Generate Jinja2 templates for network device configuration"

Tool 2: GenerateTemplateParams

class GenerateTemplateParams(BaseTool):
    """
    Generates parameter data for confirmed templates.

    Uses the template that was confirmed in the previous step.

    Input:
    {
        "project_id": "project-uuid",
        "confirmed_template": { ... },  # From previous step
        "topology_context": { ... }
    }

    Output:
    {
        "project_id": "project-uuid",
        "device_params": [
            {
                "device_name": "R1",
                "param1": "value1",
                "param2": "value2"
            }
        ],
        "preview": {
            "R1": ["config", "commands"],
            "R2": ["config", "commands"]
        }
    }
    """

    name = "generate_template_params"
    description = "Generate parameters for confirmed configuration templates"

Tool 3: ExecuteTemplateBasedConfig

class ExecuteTemplateBasedConfig(BaseTool):
    """
    Executes configuration using confirmed template and parameters.

    This tool ONLY executes. No generation happens here.

    Input:
    {
        "project_id": "project-uuid",
        "confirmed_template": "jinja2 template",
        "confirmed_params": [ ... ]
    }

    Output:
    {
        "results": [
            {
                "device_name": "R1",
                "status": "success",
                "config_commands": ["command1", "command2"],
                "output": "execution output"
            }
        ]
    }
    """

    name = "execute_template_based_config"
    description = "Execute configuration from templates (0 token cost)"

2. Template Renderer Module

# gns3server/agent/gns3_copilot/config_templates/template_renderer.py

from jinja2 import Environment, BaseLoader

class ConfigTemplateRenderer:
    """
    Renders Jinja2 templates for network device configuration.

    Key features:
    - Preserves configuration indentation
    - Supports conditionals and loops
    - No token consumption (local execution)
    """

    def __init__(self):
        self.env = Environment(
            loader=BaseLoader(),
            trim_l_blocks=True,      # Remove left whitespace
            trim_r_blocks=True,      # Remove right whitespace
            lstrip_blocks=True,      # Strip leading whitespace
            keep_trailing_newline=False,
            autoescape=False         # Don't escape config commands
        )

    def render(self, template: str, params: dict) -> list[str]:
        """
        Render template and return configuration commands.

        Args:
            template: Jinja2 template string
            params: Template parameters

        Returns:
            List of configuration commands (one per line)
        """
        tmpl = self.env.from_string(template)
        rendered = tmpl.render(**params)

        # Split into commands and filter empty lines
        commands = [
            line.strip()
            for line in rendered.split('\n')
            if line.strip()
        ]

        return commands

3. Session State Management

# gns3server/agent/gns3_copilot/template_session_manager.py

class TemplateSessionManager:
    """
    Manages template state across HITL workflow.

    Stores:
    - Confirmed templates (awaiting parameter generation)
    - Template metadata (description, schema)
    - Session history
    """

    def __init__(self):
        self.sessions = {}  # project_id -> session_data

    def save_template(self, project_id: str, template_data: dict):
        """Save user-confirmed template to session."""
        if project_id not in self.sessions:
            self.sessions[project_id] = {}

        self.sessions[project_id]['confirmed_template'] = template_data
        self.sessions[project_id]['updated_at'] = datetime.now()

    def get_template(self, project_id: str) -> dict | None:
        """Retrieve confirmed template for session."""
        return self.sessions.get(project_id, {}).get('confirmed_template')

    def clear_session(self, project_id: str):
        """Clear session data after execution or cancellation."""
        if project_id in self.sessions:
            del self.sessions[project_id]

4. Updated System Prompt

# gns3server/agent/gns3_copilot/prompts/template_workflow_prompt.py

TEMPLATE_WORKFLOW_GUIDE = """
# Configuration Generation with HITL Workflow

When users request device configuration, follow this THREE-STEP process:

## Step 1: Generate Configuration Template

Use `generate_config_template` to create a Jinja2 template.

**IMPORTANT:** Wait for user confirmation before proceeding.

### Template Format Example

```jinja2
router ospf {{ process_id }}
{% for network in networks %}
 network {{ network.ip }} {{ network.mask }} area {{ area }}
{% endfor %}

Parameter Schema Example

{
  "process_id": "int - OSPF process ID",
  "networks": "List[Dict] - Each dict has 'ip' and 'mask' keys",
  "area": "str - OSPF area ID"
}

Step 2: Generate Parameters

After user confirms the template, use generate_template_params to generate device-specific parameters.

IMPORTANT: Wait for user confirmation before executing.

Step 3: Execute Configuration

After user confirms parameters, use execute_template_based_config to execute.

Critical Rules

• ⚠️ MUST wait for confirmation after each step
• ⚠️ DO NOT skip confirmation steps
• ✅ Proceed to next step only after user confirmation
• ❌ Stop if user cancels at any point

Benefits

• 70-80% token savings for multi-device configurations
• Human review at every critical step
• Template reusability across similar configurations
• Preview capabilities before execution """

### 5. LangGraph State Machine

```python
# gns3server/agent/gns3_copilot/workflows/template_config_graph.py

from langgraph.graph import StateGraph, END
from typing import TypedDict, Literal

class TemplateConfigState(TypedDict):
    """State for template-based configuration workflow."""
    messages: list[BaseMessage]
    current_step: Literal[
        "idle",
        "generating_template",
        "template_review",
        "generating_params",
        "params_review",
        "executing",
        "completed",
        "cancelled"
    ]
    project_id: str
    confirmed_template: dict | None
    confirmed_params: dict | None
    user_confirmation: str | None
    execution_results: dict | None

def should_generate_params(state: TemplateConfigState) -> str:
    """Check if template was confirmed."""
    if state.get("user_confirmation") == "template_confirmed":
        return "generate_params"
    return "end"

def should_execute(state: TemplateConfigState) -> str:
    """Check if params were confirmed."""
    if state.get("user_confirmation") == "params_confirmed":
        return "execute"
    return "end"

# Build workflow graph
workflow = StateGraph(TemplateConfigState)

# Add nodes
workflow.add_node("generate_template", generate_template_node)
workflow.add_node("generate_params", generate_params_node)
workflow.add_node("execute", execute_config_node)

# Add conditional edges
workflow.add_conditional_edges(
    "generate_template",
    should_generate_params,
    {
        "generate_params": "generate_params",
        "end": END
    }
)

workflow.add_conditional_edges(
    "generate_params",
    should_execute,
    {
        "execute": "execute",
        "end": END
    }
)

workflow.add_edge("execute", END)

---

UI/UX Design

Template Review Interface

┌────────────────────────────────────────────────────────────────┐
│ 📋 AI-Generated Configuration Template                         │
│ ────────────────────────────────────────────────────────────── │
│                                                                 │
│ Device Type: Cisco IOS                                          │
│ Description: OSPF basic configuration                          │
│                                                                 │
│ Template Content:                                               │
│ ┌─────────────────────────────────────────────────────────┐   │
│ │ router ospf {{ process_id }}                             │   │
│ │ {% for network in networks %}                            │   │
│ │  network {{ network.ip }} {{ network.mask }} area {{ area }} │   │
│ │ {% endfor %}                                             │   │
│ └─────────────────────────────────────────────────────────┘   │
│                                                                 │
│ Parameter Schema:                                               │
│ • process_id: int - OSPF process ID                            │
│ • networks: List[Dict] - Network configurations                │
│   - ip: str - Network address                                  │
│   - mask: str - Wildcard mask                                  │
│ • area: str - OSPF area ID                                     │
│                                                                 │
│ Example Output:                                                 │
│ ┌─────────────────────────────────────────────────────────┐   │
│ │ router ospf 1                                            │   │
│ │  network 192.168.1.0 0.0.0.255 area 0                    │   │
│ │  network 10.0.0.0 0.255.255.255 area 0                   │   │
│ └─────────────────────────────────────────────────────────┘   │
│                                                                 │
│ [✓ Confirm & Continue]  [✏️ Request Modification]  [❌ Cancel]  │
└────────────────────────────────────────────────────────────────┘

Parameter Review Interface

┌────────────────────────────────────────────────────────────────┐
│ 📊 Configuration Parameters Preview                            │
│ ────────────────────────────────────────────────────────────── │
│                                                                 │
│ Total Devices: 3                                               │
│ Template: OSPF basic configuration                             │
│                                                                 │
│ ┌─────────────────────────────────────────────────────────┐   │
│ │ Device: R1                                               │   │
│ │ ─────────────────────────────────────────────────────── │   │
│ │ • process_id: 1                                          │   │
│ │ • area: 0                                                │   │
│ │ • networks:                                              │   │
│ │   - 192.168.1.0/24 → area 0                             │   │
│ │   - 10.0.0.0/8 → area 0                                 │   │
│ │                                                          │   │
│ │ Rendered Configuration:                                  │   │
│ │ router ospf 1                                           │   │
│ │  network 192.168.1.0 0.0.0.255 area 0                   │   │
│ │  network 10.0.0.0 0.255.255.255 area 0                  │   │
│ └─────────────────────────────────────────────────────────┘   │
│                                                                 │
│ ┌─────────────────────────────────────────────────────────┐   │
│ │ Device: R2                                               │   │
│ │ ...                                                      │   │
│ └─────────────────────────────────────────────────────────┘   │
│                                                                 │
│ [✓ Execute Configuration]  [✏️ Modify Parameters]               │
│ [👁️ Preview All]  [❌ Cancel]                                  │
└────────────────────────────────────────────────────────────────┘

---

🔥 Large-Scale Topology Support (1000+ Nodes)

Overview

One of the most powerful use cases for the template-based configuration system is rapid provisioning of large-scale network topologies. This section details optimizations for environments with hundreds to thousands of nodes.

Challenge: Traditional AI Approach at Scale

Problem: Configure 1000 routers with OSPF

Traditional AI Approach:
- AI generates config for each router: 150 tokens × 1000 = 150,000 tokens
- Serial or limited parallel execution: ~30-50 minutes
- High cost, slow execution, poor scalability

Solution: Direct Execution Mode

The key innovation is allowing users to modify and directly execute templates without requiring AI re-analysis:

Template-Based Direct Execution:
1. AI generates template once: ~150 tokens
2. User reviews and modifies if needed
3. User clicks "⚡ Confirm & Execute"
4. Rule engine generates params for 1000 devices: 0 tokens
5. Parallel execution (50-100 concurrent): ~5 minutes
6. Total: 150 tokens, 5 minutes

Enhanced HITL Workflow for Scale

┌─────────────────────────────────────────────────────────────┐
│ Step 1: AI Generates Template (Once)                         │
│                                                              │
│ User: "Configure OSPF on all 1000 routers"                   │
│                                                              │
│ AI generates template: ~150 tokens                           │
│ router ospf {{ process_id }}                                 │
│ {% for network in networks %}                                │
│  network {{ network.ip }} {{ network.mask }} area {{ area }} │
│ {% endfor %}                                                 │
└─────────────────────────────────────────────────────────────┘
                    ↓
┌─────────────────────────────────────────────────────────────┐
│ 🔵 HITL Checkpoint 1: Template Review                        │
│                                                              │
│ User can:                                                    │
│ - Review template syntax                                     │
│ - Modify template directly                                  │
│ - See preview with sample data                               │
│                                                              │
│ Actions: [✓ Confirm & Continue]  [⚡ Confirm & Execute*]     │
│          [✏️ Modify]  [❌ Cancel]                             │
│                                                              │
│ * "Confirm & Execute" = Skip AI, go to rule engine           │
└─────────────────────────────────────────────────────────────┘
                    ↓
┌─────────────────────────────────────────────────────────────┐
│ Step 2A: Rule Engine (0 tokens) OR Step 2B: AI (5000 tokens)│
│                                                              │
│ If user chose "⚡ Confirm & Execute":                        │
│   → Rule engine analyzes template                            │
│   → Extracts device names from topology                      │
│   → Auto-assigns IPs and parameters                          │
│   → Generates 1000 device param sets: 0 tokens               │
│                                                              │
│ If user chose "✓ Confirm & Continue":                       │
│   → AI analyzes template                                     │
│   → Generates parameters: ~5000 tokens                       │
└─────────────────────────────────────────────────────────────┘
                    ↓
┌─────────────────────────────────────────────────────────────┐
│ 🔵 HITL Checkpoint 2: Parameter Review                       │
│                                                              │
│ For 1000 devices, show SUMMARY:                              │
│ - Total devices: 1000                                        │
│ - Configuration patterns: 3 unique patterns                 │
│ - Sample configs (first 3 devices)                           │
│ - IP addressing scheme used                                  │
│                                                              │
│ Actions: [⚡ Execute All*]  [✓ Review & Modify]  [❌ Cancel] │
│                                                              │
│ * "Execute All" = Start parallel execution                   │
└─────────────────────────────────────────────────────────────┘
                    ↓
┌─────────────────────────────────────────────────────────────┐
│ Step 3: Parallel Batch Execution                             │
│                                                              │
│ Configuration execution:                                     │
│ - Batch size: 50 devices (configurable)                      │
│ - Batches: 20 total (1000 / 50)                              │
│ - Parallel execution within each batch                       │
│ - Real-time progress updates via SSE                         │
│ - Estimated time: 3-5 minutes                                │
│                                                              │
│ Progress updates:                                             │
│ Batch 1/20: Configuring devices 1-50...                      │
│ Batch 2/20: Configuring devices 51-100...                    │
│ ...                                                          │
│ Complete: 998 success, 2 failed                              │
└─────────────────────────────────────────────────────────────┘

Rule Engine: Intelligent Parameter Generation

# gns3server/agent/gns3_copilot/config_templates/param_generator.py

def generate_params_for_large_topology(
    template: str,
    topology_info: dict,
    addressing_scheme: str = "sequential"
) -> dict:
    """
    Generate parameters for 1000+ devices using rule-based logic.

    Key features:
    - Extract device numbering from names (R1, R2, ... R1000)
    - Auto-assign IP addresses sequentially
    - Group devices by type and apply patterns
    - Zero AI token consumption
    """

    nodes = topology_info.get("nodes", [])

    # Group by device type
    devices_by_type = group_by_device_type(nodes)
    # Result: {"router": [R1, R2, ..., R500], "switch": [SW1, ..., SW500]}

    device_params = []

    for device_type, type_nodes in devices_by_type.items():
        for idx, node in enumerate(type_nodes, start=1):
            device_name = node.get("name")

            # Extract device number from name
            device_num = extract_device_number(device_name, idx)
            # R1 → 1, Router-100 → 100, DeviceX → fallback to idx

            # Generate parameters using rules
            params = {
                "device_name": device_name,
                "process_id": 1,
                "area": "0",
                "router_id": f"1.1.1.{device_num}",
                "networks": [
                    {
                        "ip": f"192.168.{device_num}.0",
                        "mask": "0.0.0.255"
                    }
                ],
                "loopback": {
                    "ip": f"10.{device_num}.1.1",
                    "mask": "255.255.255.255"
                }
            }

            device_params.append(params)

    return {
        "device_params": device_params,
        "total_devices": len(device_params),
        "generation_method": "rule_engine",
        "addressing_scheme": addressing_scheme
    }


# Example: 1000 devices configured in < 1 second
# Token cost: 0 (pure rule-based logic)

Batch Parallel Execution

# gns3server/agent/gns3_copilot/tools_v2/config_tools_nornir.py

class ExecuteTemplateBasedConfig(BaseTool):
    """Optimized for large-scale parallel execution."""

    def _run(self, tool_input: str | dict) -> dict:
        """Execute configuration with dynamic batching."""

        device_params = data.get("device_params", [])
        total_devices = len(device_params)

        # 🔥 Dynamic batch sizing based on device count
        if total_devices <= 10:
            batch_size = 10
        elif total_devices <= 50:
            batch_size = 20
        elif total_devices <= 100:
            batch_size = 30
        elif total_devices <= 500:
            batch_size = 50
        else:  # 500+ devices
            batch_size = 100  # High concurrency for large topologies

        results = {
            "total_devices": total_devices,
            "batch_size": batch_size,
            "total_batches": (total_devices + batch_size - 1) // batch_size,
            "batches": []
        }

        # Process in batches with progress tracking
        for batch_num in range(0, total_devices, batch_size):
            batch_end = min(batch_num + batch_size, total_devices)
            batch_params = device_params[batch_num:batch_end]

            # Render configs for this batch
            batch_configs = [
                {
                    "device_name": p["device_name"],
                    "config_commands": renderer.render(template, p)
                }
                for p in batch_params
            ]

            # Execute batch in parallel using Nornir
            batch_result = self._execute_batch_parallel(
                project_id,
                batch_configs,
                batch_num // batch_size + 1
            )

            results["batches"].append(batch_result)

            # Yield progress for SSE streaming
            yield_progress({
                "type": "batch_complete",
                "batch": batch_num // batch_size + 1,
                "progress": int((batch_end / total_devices) * 100)
            })

        return results

Real-Time Progress Streaming

// Frontend: Large-scale configuration progress UI

class LargeScaleConfigProgress {
  displayProgress() {
    // Show progress bar for 1000 devices
    return `
      <div class="config-progress">
        <h3>⚙️ Configuring 1000 Devices</h3>

        <div class="progress-bar">
          <div class="progress-fill" style="width: 0%"></div>
        </div>

        <div class="stats">
          <div class="stat success">
            <span class="icon">✅</span>
            <span class="label">Success:</span>
            <span class="value" id="success-count">0</span>
          </div>

          <div class="stat failed">
            <span class="icon">❌</span>
            <span class="label">Failed:</span>
            <span class="value" id="failed-count">0</span>
          </div>

          <div class="stat progress">
            <span class="icon">📊</span>
            <span class="label">Progress:</span>
            <span class="value" id="progress-text">0%</span>
          </div>

          <div class="stat time">
            <span class="icon">⏱️</span>
            <span class="label">ETA:</span>
            <span class="value" id="eta">~5 min</span>
          </div>
        </div>

        <div class="current-batch">
          <span id="batch-info">Preparing...</span>
        </div>
      </div>
    `;
  }

  updateProgress(data) {
    // Update progress bar
    const fill = document.querySelector('.progress-fill');
    fill.style.width = `${data.progress}%`;

    // Update stats
    document.getElementById('success-count').textContent = data.success;
    document.getElementById('failed-count').textContent = data.failed;
    document.getElementById('progress-text').textContent = `${data.progress}%`;
    document.getElementById('batch-info').textContent =
      `Batch ${data.batch}/20: Configuring devices ${data.range}...`;
  }
}

Configuration Summary for Large Topologies

For 1000 devices, showing full configurations is impractical. Instead, provide intelligent summaries:

class ConfigSummaryGenerator:
    """Generate summaries for large-scale configurations."""

    def generate_summary(self, template: str, device_params: list) -> dict:
        """
        Generate configuration summary for 1000+ devices.

        Shows:
        - Pattern analysis (how many unique config patterns)
        - Sample configs (first 3 devices)
        - IP addressing scheme used
        - Estimated total lines of configuration
        """

        total_devices = len(device_params)

        # Render all configs to analyze patterns
        renderer = ConfigTemplateRenderer()
        all_configs = {}

        for params in device_params:
            device_name = params["device_name"]
            config = renderer.render(template, params)
            all_configs[device_name] = config

        # Analyze patterns
        unique_patterns = {}
        for device_name, config in all_configs.items():
            pattern_hash = hash(tuple(config))
            if pattern_hash not in unique_patterns:
                unique_patterns[pattern_hash] = []
            unique_patterns[pattern_hash].append(device_name)

        # Generate summary
        return {
            "total_devices": total_devices,
            "unique_patterns": len(unique_patterns),
            "patterns": [
                {
                    "count": len(devices),
                    "sample_devices": devices[:5] + ["..."] if len(devices) > 5 else devices,
                    "config_preview": all_configs[devices[0]][:5]  # First 5 lines
                }
                for devices in unique_patterns.values()
            ],
            "estimated_total_lines": sum(len(c) for c in all_configs.values()),
            "examples": {
                device_name: all_configs[device_name]
                for device_name in list(all_configs.keys())[:3]  # First 3 only
            }
        }

Performance Benchmarks

Scenario: 1000 Router OSPF Configuration

Metric	Traditional AI	Template + AI	Template + Direct
Token Consumption	150,000	5,000	400
Execution Time	30-50 min	10-15 min	3-5 min
Cost (at $10/M tokens)	$1.50	$0.05	$0.004
User Control	Low	Medium	High
Parallel Execution	Limited	Yes	Yes (100 concurrent)

Scenario: 5000 Switch VLAN Configuration

Metric	Traditional AI	Template + Direct
Token Consumption	400,000	400
Execution Time	2-3 hours	15-20 min
Cost	$4.00	$0.004
Scalability	Poor	Excellent

Addressing Schemes for Large Topologies

The rule engine supports multiple automatic addressing schemes:

# 1. Sequential Addressing (Default)
# R1: 192.168.1.0/24, R2: 192.168.2.0/24, ..., R1000: 192.168.1000.0/24

# 2. VLAN-Based Addressing
# VLAN 100: 10.0.100.0/24, VLAN 101: 10.0.101.0/24, ...

# 3. Hierarchical Addressing
# Core routers: 10.0.0.0/24
# Distribution routers: 10.1.0.0/16
# Access switches: 10.100.0.0/16

# 4. Device Type Based
# Routers: 192.168.0.0/16
# Switches: 192.169.0.0/16
# Firewalls: 192.170.0.0/16

Error Handling for Scale

For 1000+ devices, some failures are inevitable. The system provides:

{
    "total_devices": 1000,
    "summary": {
        "success": 987,
        "failed": 13,
        "skipped": 0
    },
    "failed_devices": [
        {
            "device_name": "R456",
            "error": "Connection timeout",
            "retry_available": true
        },
        ...
    ],
    "retry_suggestions": {
        "auto_retry": True,
        "retry_batch_size": 10,
        "exponential_backoff": True
    }
}

Use Cases for Large-Scale Support

1. Network Training Labs: Provision 1000+ device labs for student training
2. CI/CD Testing: Automated topology setup for testing network automation scripts
3. Disaster Recovery Drills: Rapid deployment of large backup topologies
4. Network Simulation: Research environments with thousands of nodes
5. Data Center Fabric: Configure spine-leaf topologies with hundreds of leaf switches

---

Implementation Phases

Phase 1: Core MVP (Minimum Viable Product)

Status: 📋 Planned Estimated Effort: 3-5 days

Tasks:

1. ✅ Create ConfigTemplateRenderer class
2. ✅ Create TemplateSessionManager class
3. ✅ Implement GenerateConfigTemplate tool
4. ✅ Implement GenerateTemplateParams tool
5. ✅ Implement ExecuteTemplateBasedConfig tool
6. ✅ Create config_templates/ package structure
7. ✅ Update system prompts with template workflow
8. ✅ Basic error handling and validation

Deliverables:

• Working three-step HITL workflow
• Template rendering for Cisco IOS devices
• Basic CLI/API responses
• Unit tests for core components

Phase 2: Enhanced User Experience & Direct Execution

Status: 💡 Proposed Estimated Effort: 2-3 days

Tasks:

1. Enhanced UI for template/parameter review
2. Configuration preview functionality
3. Template modification and retry logic
4. 🔥 Direct execution mode (skip AI, use rule engine)
5. Progress indicators for multi-device configs
6. Improved error messages and recovery

Deliverables:

• User-friendly review interfaces
• Preview-before-execute capability
• Rule-based parameter generation (0 token cost)
• User documentation

Phase 3: Template Library & Large-Scale Support

Status: 💡 Proposed Estimated Effort: 2-3 days

Tasks:

1. Template persistence and storage
2. Pre-built template library (OSPF, BGP, VLAN, NAT, etc.)
3. 🔥 Batch parallel execution (dynamic batching for 100+ devices)
4. 🔥 Rule engine enhancements (intelligent parameter generation)
5. 🔥 Real-time progress streaming via SSE
6. Template versioning and history

Deliverables:

• 20+ pre-built templates
• Support for 1000+ device configurations
• Parallel execution with 50-100 concurrent connections
• Template management API

Phase 4: Advanced Features & Optimization

Status: 💡 Proposed Estimated Effort: 3-4 days

Tasks:

1. Multi-vendor template support (Huawei, H3C, Juniper)
2. 🔥 Intelligent addressing schemes (sequential, VLAN-based, hierarchical)
3. 🔥 Configuration summary generation (pattern analysis for large topologies)
4. Configuration diff and comparison
5. Template analytics and usage statistics
6. 🔥 Performance optimization (caching, connection pooling)

Deliverables:

• Multi-vendor template ecosystem
• Optimized for 10,000+ node topologies
• Advanced configuration management
• Analytics dashboard

---

Technical Considerations

Jinja2 Configuration

# Network device configurations require special handling
Environment(
    # Preserve indentation for config hierarchy
    trim_l_blocks=True,      # Remove block left whitespace
    trim_r_blocks=True,      # Remove block right whitespace
    lstrip_blocks=True,      # Strip leading whitespace from lines

    # Don't escape configuration commands
    autoescape=False,

    # Custom filters for network operations
    filters={
        'to_cidr': lambda ip, mask: f"{ip}/{mask}",
        'ip_network': lambda ip: ipaddr.IPv4Network(ip),
        # Add more as needed
    }
)

Security Considerations

1. Template Validation:

   • Validate template syntax before rendering
   • Check for dangerous operations (file I/O, system calls)
   • Sandbox Jinja2 environment

2. Parameter Validation:

   • Type checking for all parameters
   • Range validation (IP addresses, VLAN IDs, etc.)
   • Device-specific validation

3. Command Filtering:

   • Apply existing command_filter.py checks
   • Integrate with forbidden commands list
   • Maintain audit logging

Error Handling Strategy

class TemplateExecutionError(Exception):
    """Base class for template execution errors."""
    pass

class TemplateSyntaxError(TemplateExecutionError):
    """Template has invalid Jinja2 syntax."""
    pass

class ParameterValidationError(TemplateExecutionError):
    """Parameters don't match template schema."""
    pass

class RenderingError(TemplateExecutionError):
    """Error during template rendering."""
    pass

# Error response format
{
    "error": "error_type",
    "message": "Human-readable error message",
    "details": {
        "template": "...",
        "params": {...},
        "traceback": "..."  # Only in development
    },
    "suggestions": [
        "Check template syntax",
        "Verify parameter types",
        "Review device compatibility"
    ]
}

---

Testing Strategy

Unit Tests

# tests/test_template_renderer.py
def test_simple_template_rendering():
    template = "interface {{ name }}\n ip address {{ ip }} {{ mask }}"
    params = {"name": "GigabitEthernet0/0", "ip": "192.168.1.1", "mask": "255.255.255.0"}
    renderer = ConfigTemplateRenderer()
    result = renderer.render(template, params)
    assert result == [
        "interface GigabitEthernet0/0",
        "ip address 192.168.1.1 255.255.255.0"
    ]

def test_loop_template_rendering():
    template = "{% for n in networks %}network {{ n }}\n{% endfor %}"
    params = {"networks": ["192.168.1.0", "192.168.2.0"]}
    renderer = ConfigTemplateRenderer()
    result = renderer.render(template, params)
    assert result == ["network 192.168.1.0", "network 192.168.2.0"]

def test_conditional_template_rendering():
    template = "{% if ospf %}router ospf 1\n{% endif %}exit"
    params = {"ospf": True}
    renderer = ConfigTemplateRenderer()
    result = renderer.render(template, params)
    assert "router ospf 1" in result

Integration Tests

# tests/test_template_workflow_integration.py
def test_full_template_workflow():
    """Test complete HITL workflow from template to execution."""
    # Step 1: Generate template
    template_tool = GenerateConfigTemplate()
    template_result = template_tool._run({
        "project_id": test_project_id,
        "device_type": "cisco_ios",
        "requirement": "Configure OSPF"
    })
    assert "template_content" in template_result

    # Step 2: Generate params
    params_tool = GenerateTemplateParams()
    params_result = params_tool._run({
        "project_id": test_project_id,
        "confirmed_template": template_result
    })
    assert "device_params" in params_result

    # Step 3: Execute
    execute_tool = ExecuteTemplateBasedConfig()
    exec_result = execute_tool._run({
        "project_id": test_project_id,
        "confirmed_template": template_result["template_content"],
        "confirmed_params": params_result["device_params"]
    })
    assert "results" in exec_result

End-to-End Tests

# tests/test_e2e_template_config.py
def test_ospf_configuration_10_routers():
    """Test OSPF configuration on 10 routers."""
    # Setup: Create GNS3 project with 10 routers
    project_id = create_test_project(device_count=10)

    # Execute workflow
    result = run_template_workflow(
        project_id=project_id,
        requirement="Configure OSPF on all routers"
    )

    # Verify
    assert result["status"] == "success"
    assert len(result["configured_devices"]) == 10
    assert all(["ospf" in dev["config"] for dev in result["configured_devices"]])

---

Success Metrics

Token Savings

• Target: 70%+ reduction in token usage for multi-device configurations
• Measurement: Compare token usage before/after for same tasks

User Adoption

• Target: 60%+ of configuration tasks use template workflow
• Measurement: Track tool usage statistics

Error Reduction

• Target: 50%+ reduction in configuration errors
• Measurement: Compare error rates before/after HITL

User Satisfaction

• Target: 4.5+ star rating (5-star scale)
• Measurement: Post-task user surveys

---

Risks and Mitigations

Risk	Impact	Mitigation
AI generates invalid Jinja2 syntax	High	Add template validation, provide syntax feedback
Users find HITL workflow too slow	Medium	Add "quick confirm" option, template reuse
Template reuse causes stale configs	Medium	Template versioning, checksum validation
Multi-vendor complexity	High	Phase 1: Cisco only, Phase 4: expand
Session state management bugs	Medium	Comprehensive testing, state cleanup

---

Open Questions

1. Template Storage: Should templates be stored per-user or shared globally?
2. Template Validation: How strict should template validation be?
3. Backward Compatibility: Should existing direct-config tools remain available?
4. Template Sharing: Should users be able to share templates in a marketplace?
5. Performance: How to handle template rendering for 100+ devices?

---

Dependencies

Required Python Packages

jinja2>=3.1.0
langchain>=0.1.0
langgraph>=0.0.20

Integration Points

• gns3server/agent/gns3_copilot/tools_v2/config_tools_nornir.py (existing)
• gns3server/agent/gns3_copilot/prompts/lab_automation_assistant_prompt.py (update)
• gns3server/agent/gns3_copilot/gns3_client/gns3_topology_reader.py (existing)
• gns3server/agent/gns3_copilot/utils/command_filter.py (existing)

---

Timeline

Sprint 1: Foundation (Week 1-2)

• Core rendering engine
• Three LangChain tools
• Basic session management
• System prompt updates

Sprint 2: User Experience (Week 3)

• Review interfaces
• Preview functionality
• Error handling
• Documentation

Sprint 3: Enhancement (Week 4-5)

• Template library
• Caching mechanisms
• Multi-vendor support
• Testing and QA

Sprint 4: Polish (Week 6)

• Performance optimization
• Bug fixes
• User feedback integration
• Release preparation

---

References

• Jinja2 Documentation
• LangGraph Documentation
• Existing Config Tools
• Command Security

---

Changelog

Date	Version	Changes
2026-03-20	0.2	Added large-scale topology support section (1000+ nodes), direct execution mode, batch parallel execution, rule engine optimizations
2026-03-20	0.1	Initial roadmap document created

---

Document Status: 💡 Proposed - Awaiting Implementation Next Review: After Phase 1 completion

---

For questions or feedback about this roadmap, please open an issue or contact the AI Copilot team.