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This documentation is organized by AI with reference to actual code. AI can make mistakes — please verify against the source code when in doubt.
Template-Based System with HITL - Future Roadmap
Status: 💡 Proposed Target Version: Next Release Last Updated: 2026-03-20
Overview
This document outlines the plan for implementing template-based systems with Human-in-the-Loop (HITL) confirmations for both device configuration and node creation in GNS3 AI Copilot.
Scope & Positioning
This system focuses on baseline configuration and topology provisioning - getting from zero to a manageable state. Once devices are connected and have basic IP/routing configuration, modern network management tools can take over for production-grade configuration management.
┌─────────────────────────────────────────────────────────────┐
│ Phase 1: Environment Preparation (This System) │
│ ─────────────────────────────────────────────────────────── │
│ • Create topology (nodes + links) │
│ • Baseline IP configuration │
│ • Enable routing protocols (OSPF/BGP) │
│ • Management access (SSH/HTTPS/NETCONF) │
│ • Basic security (ACLs, passwords) │
│ ─────────────────────────────────────────────────────────── │
│ Result: Manageable network ready for production tools │
└─────────────────────────────────────────────────────────────┘
↓
┌─────────────────────────────────────────────────────────────┐
│ Phase 2: Production Configuration (External Tools) │
│ ─────────────────────────────────────────────────────────── │
│ • Terraform (Infrastructure as Code) │
│ • REST API (Modern device management) │
│ • NETCONF/YANG (Standardized configuration) │
│ • Network Controllers (SDN, APIC, etc.) │
│ • Monitoring & Observability │
└─────────────────────────────────────────────────────────────┘
Use Cases for This System:
- Rapid lab provisioning (training, testing, CI/CD)
- Network simulation and research
- Proof-of-concept deployments
- Disaster recovery drills
- Initial topology setup before handoff to automation tools
Not In Scope (handled by other tools):
- Fine-grained configuration management
- Compliance and policy enforcement
- Continuous configuration drift management
- Production-grade change management
- Advanced telemetry and monitoring
Motivation
Current Configuration Challenges
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
Current Node Creation Challenges
Similarly, creating multiple nodes has significant inefficiencies:
- Token waste: Each node creation requires ~50 tokens for tool calls (100 nodes = 5000 tokens)
- Slow execution: Nodes are created serially or with limited parallelism
- No batch operations: Cannot create groups of related nodes efficiently
- Manual positioning: Each node must be positioned individually
Proposed Solution
Implement a unified template-based HITL workflow for both configuration and node creation:
- AI generates template → Human reviews and confirms
- AI generates parameters (optional) → Human reviews and confirms
- Local execution → Results displayed
Expected Benefits:
- 98-99% token savings for large-scale operations (1000+ devices/nodes)
- 90%+ time savings through parallel execution and batch operations
- Full user control with preview and confirmation at every step
- Template reusability across similar operations
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
System Architecture Overview
┌─────────────────────────────────────────────────────────────────────┐
│ GNS3 Web UI / CLI │
└──────────────────────────────┬──────────────────────────────────────┘
│ HTTP/WebSocket
↓
┌─────────────────────────────────────────────────────────────────────┐
│ GNS3 Server (FastAPI) │
│ │
│ ┌─────────────┐ ┌──────────────┐ ┌──────────────────────────┐ │
│ │ Chat API │ │ Template API │ │ SSE Progress Stream │ │
│ │ (existing) │ │ (new) │ │ (new) │ │
│ └──────┬──────┘ └──────┬───────┘ └──────────┬───────────────┘ │
│ │ │ │ │
│ └────────────────┴─────────────────────┘ │
│ │ │
└───────────────────────────────┼─────────────────────────────────────┘
│
↓
┌─────────────────────────────────────────────────────────────────────┐
│ AI Copilot Agent (LangGraph) │
│ │
│ ┌──────────────────────────────────────────────────────────────┐ │
│ │ HITL Workflow Orchestrator │ │
│ │ ┌──────────┐ ┌──────────┐ ┌──────────┐ │ │
│ │ │ Generate │ → │ Generate │ → │ Execute │ │ │
│ │ │ Template │ │ Params │ │ Config │ │ │
│ │ └────┬─────┘ └────┬─────┘ └────┬─────┘ │ │
│ │ │ │ │ │ │
│ │ 🔵 HITL Checkpoints (LangGraph Interrupts) │ │
│ └──────────────────────────────────────────────────────────────┘ │
│ │
│ ┌──────────────────────────────────────────────────────────────┐ │
│ │ Core Modules │ │
│ │ ┌──────────────┐ ┌─────────────┐ ┌──────────────────┐ │ │
│ │ │ Template │ │ Session │ │ Rule Engine │ │ │
│ │ │ Renderer │ │ Manager │ │ (Direct Mode) │ │ │
│ │ └──────────────┘ └─────────────┘ └──────────────────┘ │ │
│ └──────────────────────────────────────────────────────────────┘ │
└─────────────────────────────────────────────────────────────────────┘
│
↓
┌─────────────────────────────────────────────────────────────────────┐
│ GNS3 Controller & Compute │
│ ┌────────────┐ ┌────────────┐ ┌──────────────────────────┐ │
│ │ Node │ │ Link │ │ Nornir + Netmiko │ │
│ │ Management │ │ Management │ │ (Config Execution) │ │
│ └────────────┘ └────────────┘ └──────────────────────────┘ │
└─────────────────────────────────────────────────────────────────────┘
HITL State Transition Diagram
┌─────────────┐
│ IDLE │
└──────┬──────┘
│ User Request
↓
┌─────────────┐
│ GENERATING │
│ TEMPLATE │
└──────┬──────┘
│ AI Complete
↓
┌─────────────────────────────────┐
│ 🔵 TEMPLATE_REVIEW │
│ (LangGraph Interrupt) │
│ │
│ User sees: │
│ - Template content │
│ - Parameter schema │
│ - Example output │
│ │
│ Actions: │
│ [Confirm] [Modify] [Cancel] │
└─────┬───────────────┬───────────┘
│ │
Confirm │ │ Cancel
│ ↓
┌──────┴──────┐ ┌────────┐
│ GENERATING │ │ END │
│ PARAMS │ └────────┘
└──────┬──────┘
│ AI Complete OR
│ Rule Engine
↓
┌─────────────────────────────────┐
│ 🔵 PARAMS_REVIEW │
│ (LangGraph Interrupt) │
│ │
│ User sees: │
│ - Device list │
│ - Parameters per device │
│ - Rendered configs │
│ │
│ Actions: │
│ [Execute] [Modify] [Cancel] │
└─────┬───────────────┬───────────┘
│ │
Execute │ │ Cancel
│ ↓
┌──────┴──────┐ ┌────────┐
│ EXECUTING │ │ END │
│ (0 tokens) │ └────────┘
└──────┬──────┘
│ Complete
↓
┌─────────────┐
│ COMPLETED │
└─────────────┘
Component Overview
1. LangChain Tools (3 new tools)
GenerateConfigTemplate
- Purpose: Generate Jinja2 templates for human review
- Input: project_id, device_type, requirement
- Output: template_content, description, params_schema, rendered_example
- Token Cost: ~150-200 tokens
GenerateTemplateParams
- Purpose: Generate parameters for confirmed templates
- Input: project_id, confirmed_template, topology_context
- Output: device_params array with rendered previews
- Token Cost: ~50-100 tokens/device (or 0 with rule engine)
ExecuteTemplateBasedConfig
- Purpose: Execute configuration from templates (local rendering)
- Input: project_id, confirmed_template, confirmed_params
- Output: execution results per device
- Token Cost: 0 tokens (pure local execution)
2. Template Renderer Module
Key Features:
- Jinja2-based configuration rendering
- Preserves network config indentation
- Supports conditionals, loops, filters
- Zero token consumption (local execution)
Supported Template Features:
# Variables
hostname {{ hostname }}
# Loops
{% for interface in interfaces %}
interface {{ interface.name }}
ip address {{ interface.ip }} {{ interface.mask }}
{% endfor %}
# Conditionals
{% if ospf_enabled %}
router ospf {{ process_id }}
network {{ networks }} area {{ area }}
{% endif %}
# Filters
{{ ip | ip_network }} # Custom filter for IP operations
3. Session State Management
Stores:
- Confirmed templates (awaiting params)
- Template metadata (schema, description)
- Session history (for audit trail)
- User modification tracking
Lifecycle:
- Created when template generated
- Updated when user confirms/modifies
- Cleared after execution or cancellation
- TTL: 24 hours (auto-cleanup)
4. LangGraph Workflow Integration
Interrupt Mechanism:
# LangGraph interrupt points for HITL
@interrupt
def template_review_checkpoint(state):
"""Pause and wait for user confirmation."""
return {
"type": "template_review",
"data": state["generated_template"]
}
@interrupt
def params_review_checkpoint(state):
"""Pause and wait for user confirmation."""
return {
"type": "params_review",
"data": state["generated_params"]
}
State Management:
- State persisted across interrupts
- User can modify state before resuming
- Full audit trail of all transitions
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] │
└────────────────────────────────────────────────────────────────┘
API Design & Data Flow
REST API Endpoints
POST /api/v3/projects/{project_id}/templates/config
├─ Request: { "device_type": "cisco_ios", "requirement": "Configure OSPF" }
└─ Response: { "template_id": "uuid", "template_content": "...", "params_schema": {...} }
PUT /api/v3/projects/{project_id}/templates/{template_id}/confirm
├─ Request: { "action": "confirm" | "modify", "modifications": {...} }
└─ Response: { "status": "confirmed", "next_step": "generate_params" }
POST /api/v3/projects/{project_id}/templates/{template_id}/params
├─ Request: { "mode": "ai" | "direct" }
└─ Response: { "device_params": [...], "preview": {...} }
POST /api/v3/projects/{project_id}/templates/{template_id}/execute
├─ Request: { "confirmed_params": [...] }
└─ Response: { "execution_id": "uuid", "status": "executing" }
GET /api/v3/projects/{project_id}/templates/{template_id}/status
└─ Response: { "status": "completed", "progress": 100, "results": [...] }
DELETE /api/v3/projects/{project_id}/templates/{template_id}
└─ Response: { "status": "cancelled" }
SSE Progress Stream
// Server-Sent Events for real-time progress
// Endpoint: GET /api/v3/projects/{project_id}/templates/{template_id}/stream
// Event Types:
event: template_generated
data: {"template_id": "uuid", "content": "..."}
event: params_generated
data: {"total_devices": 100, "params": [...]}
event: execution_progress
data: {
"type": "batch_complete",
"batch": 5,
"total_batches": 10,
"progress": 50,
"success": 48,
"failed": 2,
"current_device": "R50"
}
event: execution_complete
data: {
"total_devices": 100,
"success": 98,
"failed": 2,
"duration_sec": 180
}
Data Flow Diagram
User Request
↓
┌─────────────────────────────────────────────────────────────┐
│ 1. API Layer (FastAPI) │
│ - Validates request │
│ - Creates session state │
│ - Returns template_id │
└──────────────────────┬──────────────────────────────────────┘
↓
┌─────────────────────────────────────────────────────────────┐
│ 2. AI Agent (LangGraph) │
│ - Generate template (LLM call) │
│ - Store in session manager │
│ - Trigger interrupt 🔵 │
└──────────────────────┬──────────────────────────────────────┘
↓
┌─────────────────────────────────────────────────────────────┐
│ 3. HITL Checkpoint (Frontend Display) │
│ - Show template to user │
│ - Wait for user action │
│ - [Confirm] [Modify] [Cancel] │
└──────────────────────┬──────────────────────────────────────┘
↓ (User confirms)
┌─────────────────────────────────────────────────────────────┐
│ 4. AI Agent (LangGraph Resumes) │
│ Path A: Generate params (LLM) ~5000 tokens │
│ Path B: Rule engine (0 tokens) ⚡ │
│ - Trigger interrupt 🔵 │
└──────────────────────┬──────────────────────────────────────┘
↓
┌─────────────────────────────────────────────────────────────┐
│ 5. HITL Checkpoint (Frontend Display) │
│ - Show parameters to user │
│ - Render configuration preview │
│ - [Execute] [Modify] [Cancel] │
└──────────────────────┬──────────────────────────────────────┘
↓ (User executes)
┌─────────────────────────────────────────────────────────────┐
│ 6. Execution Engine (Local, 0 tokens) │
│ - Render templates (Jinja2) │
│ - Batch execution (Nornir + Netmiko) │
│ - Stream progress via SSE │
└──────────────────────┬──────────────────────────────────────┘
↓
┌─────────────────────────────────────────────────────────────┐
│ 7. Result Aggregation │
│ - Collect results from all devices │
│ - Generate summary report │
│ - Clean up session state │
└──────────────────────┬──────────────────────────────────────┘
↓
Return to User
Error Handling Flow
┌─────────────────────────────────────────────────────────────┐
│ Error Detection at Each Stage │
└─────────────────────────────────────────────────────────────┘
Template Generation Error:
├─ Invalid Jinja2 syntax → [AI Retry] + [Show Error Context]
├─ Incomplete template → [Request Clarification]
└─ LLM timeout → [Retry] + [Fallback to template library]
Parameter Generation Error:
├─ Missing device data → [Fetch from topology]
├─ Invalid parameter values → [Validation Error] → [User Correction]
└─ Rule engine failure → [Fallback to AI generation]
Execution Error:
├─ Device unreachable → [Retry 3x] → [Mark as failed] → [Continue]
├─ Invalid command → [Show error] → [Suggest fix] → [User decision]
└─ Authentication failure → [Pause] → [Request credentials]
Error Recovery Strategies:
├─ Automatic retry (transient errors)
├─ Partial success handling (continue with remaining devices)
├─ Rollback support (undo partial changes)
└─ User notification (SSE + UI alerts)
Template Lifecycle Management
┌─────────────────────────────────────────────────────────────┐
│ Template Lifecycle │
└─────────────────────────────────────────────────────────────┘
1. DRAFT
├─ Created by AI
├─ Stored in session (temporary)
└─ User reviews and modifies
2. CONFIRMED
├─ User approved template
├─ Stored in template library (persistent)
└─ Ready for parameter generation
3. ACTIVE
├─ Parameters generated
├─ Ready for execution
└─ Can be cloned for similar tasks
4. EXECUTED
├─ Configuration applied
├─ Results recorded
└─ Move to archive
5. ARCHIVED
├─ Historical record
├─ Analytics data
└─ Cleanup after 90 days
Version Control:
├─ Each save creates new version
├─ Semantic versioning (v1.0, v1.1, v2.0)
├─ Diff view between versions
└─ Rollback to previous version
🔥 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
Concept: Use rule-based logic instead of AI for generating parameters in large topologies.
How It Works:
Input: Template + Topology (1000 devices)
↓
Rule Engine (0 tokens)
↓
Device Analysis
├─ Extract numbering from names (R1 → 1, R2 → 2, ...)
├─ Group by device type (routers, switches, firewalls)
├─ Apply addressing scheme (sequential, VLAN-based, hierarchical)
└─ Generate parameters for each device
↓
Output: 1000 device parameter sets (< 1 second)
Addressing Schemes:
1. Sequential (Default)
R1: 192.168.1.0/24
R2: 192.168.2.0/24
...
R1000: 192.168.1000.0/24
2. VLAN-Based
VLAN 100: 10.0.100.0/24
VLAN 101: 10.0.101.0/24
...
3. Hierarchical
Core routers: 10.0.0.0/24
Distribution: 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
Batch Parallel Execution
Dynamic Batching Strategy:
Device Count Batch Size Concurrency Estimated Time
────────────────────────────────────────────────────────────
1-10 10 10 < 30 seconds
11-50 20 20 < 1 minute
51-100 30 30 1-2 minutes
101-500 50 50 2-5 minutes
500+ 100 100 3-8 minutes
Execution Flow:
┌─────────────────────────────────────────────────────────────┐
│ Batch 1: Devices 1-100 │
│ ├─ Render 100 configs (Jinja2, local) │
│ ├─ Execute in parallel (Nornir + Netmiko) │
│ ├─ Collect results │
│ └─ Stream progress: "Batch 1/10 complete, 35% done" │
├─────────────────────────────────────────────────────────────┤
│ Batch 2: Devices 101-200 │
│ └─ ... │
├─────────────────────────────────────────────────────────────┤
│ ... │
├─────────────────────────────────────────────────────────────┤
│ Batch 10: Devices 901-1000 │
│ └─ Complete: 997 success, 3 failed │
└─────────────────────────────────────────────────────────────┘
Configuration Summary for Large Topologies
Challenge: Showing 1000 device configurations is impractical.
Solution: Intelligent summaries with pattern analysis.
┌─────────────────────────────────────────────────────────────┐
│ Configuration Summary: 1000 Devices │
├─────────────────────────────────────────────────────────────┤
│ Total Devices: 1,000 │
│ Unique Patterns: 3 │
│ Total Config Lines: ~15,000 │
│ Estimated Time: ~5 minutes │
├─────────────────────────────────────────────────────────────┤
│ Pattern Analysis: │
│ • Pattern A (650 devices): Standard OSPF config │
│ • Pattern B (300 devices): OSPF + BGP │
│ • Pattern C (50 devices): OSPF + BGP + MPLS │
├─────────────────────────────────────────────────────────────┤
│ Sample Configurations (first 3): │
│ ┌─────────────────────────────────────────────────────────┐ │
│ │ Device: R1 (Pattern A) │ │
│ │ router ospf 1 │ │
│ │ network 192.168.1.0 0.0.0.255 area 0 │ │
│ │ network 10.1.1.1 0.0.0.0 area 0 │ │
│ └─────────────────────────────────────────────────────────┘ │
│ ┌─────────────────────────────────────────────────────────┐ │
│ │ Device: R2 (Pattern A) │ │
│ │ [Similar to R1, different IPs] │ │
│ └─────────────────────────────────────────────────────────┘ │
│ ... │
└─────────────────────────────────────────────────────────────┘
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
- Network Training Labs: Provision 1000+ device labs for student training
- CI/CD Testing: Automated topology setup for testing network automation scripts
- Disaster Recovery Drills: Rapid deployment of large backup topologies
- Network Simulation: Research environments with thousands of nodes
- Data Center Fabric: Configure spine-leaf topologies with hundreds of leaf switches
🔥🔥 Node Creation Templates (Batch Topology Provisioning)
Overview
Just as configuration templates enable rapid device configuration, node creation templates enable rapid topology provisioning. This is particularly valuable for:
- Training labs: Provision 100+ device labs in minutes
- Testing environments: Quickly spin up complex test topologies
- Data center simulation: Create spine-leaf fabrics with hundreds of nodes
- Network research: Deploy large-scale simulation topologies
Current vs. Template-Based Node Creation
Scenario: Create 100 Routers
Current Method:
AI calls create_node tool 100 times:
- Token cost: 50 tokens/node × 100 = 5000 tokens
- Execution time: 5-10 minutes (serial/limited parallel)
- No batch operations
- Manual positioning required
Template Method:
1. AI generates node creation template: ~100 tokens
2. User reviews and confirms template
3. Rule engine creates nodes in parallel batches: 0 tokens
4. Total: 100 tokens, 30-60 seconds
Savings: 98% tokens, 90% time
Node Creation Workflow
User Request: "Create a data center topology with 2 core routers,
10 aggregation switches, and 100 access switches"
↓
┌─────────────────────────────────────────────────────────────┐
│ Step 1: AI Generates Node Creation Template │
│ │
│ AI Output: │
│ { │
│ "node_groups": [ │
│ { │
│ "node_type": "cisco_iosv", │
│ "count": 2, │
│ "name_pattern": "Core-R{{ id }}", │
│ "properties": {"ram": 4096, "cpus": 2}, │
│ "position": {"y": 100, "x_spacing": 600} │
│ }, │
│ { │
│ "node_type": "cisco_iosv_l2", │
│ "count": 10, │
│ "name_pattern": "Agg-SW{{ id }}", │
│ "position": {"grid": "2x5", "y": 300} │
│ }, │
│ { │
│ "node_type": "cisco_iosv_l2", │
│ "count": 100, │
│ "name_pattern": "Acc-SW{{ id }}", │
│ "position": {"grid": "10x10", "y": 600} │
│ } │
│ ], │
│ "layout": "auto_spine_leaf", │
│ "resource_limits": {"max_ram_mb": 120000} │
│ } │
└─────────────────────────────────────────────────────────────┘
↓
┌─────────────────────────────────────────────────────────────┐
│ 🔵 HITL Checkpoint: Node Template Review │
│ │
│ User sees: │
│ • Total nodes: 112 │
│ • Group breakdown: │
│ - 2x Core routers (Core-R1, Core-R2) │
│ - 10x Aggregation switches (Agg-SW1 - Agg-SW10) │
│ - 100x Access switches (Acc-SW1 - Acc-SW100) │
│ • Resource requirements: │
│ - RAM: ~120 GB │
│ - vCPUs: 112 │
│ • Layout preview (visual diagram) │
│ │
│ Actions: [⚡ Batch Create] [✏️ Modify] [❌ Cancel] │
└─────────────────────────────────────────────────────────────┘
↓
┌─────────────────────────────────────────────────────────────┐
│ Step 2: Parallel Batch Node Creation (0 tokens) │
│ │
│ Process: │
│ - Validate resources │
│ - Create nodes in parallel batches (20-50 concurrent) │
│ - Auto-position nodes using layout strategy │
│ - Real-time progress streaming │
│ │
│ Progress: │
│ Batch 1/6: Creating 20 nodes... │
│ Batch 2/6: Creating 20 nodes... │
│ ... │
│ Complete: 112/112 nodes created successfully │
└─────────────────────────────────────────────────────────────┘
Node Template Schema
Concept: Define groups of similar nodes with positioning and auto-linking.
Schema Structure:
NodeCreationTemplate
├─ node_groups: List[NodeGroup]
│ ├─ node_type: "cisco_iosv" | "vpcs" | ...
│ ├─ count: 100
│ ├─ name_pattern: "R{{ id }}" → R1, R2, ..., R100
│ ├─ properties: {ram, cpus, adapters}
│ └─ position: {strategy, grid, spacing}
├─ layout: "auto_grid" | "auto_spine_leaf" | "auto_star" | ...
├─ auto_link: AutoLinkConfig
│ └─ links: List[LinkPattern]
└─ resource_limits: {max_ram_mb, max_vcpus}
Example: Spine-Leaf Topology
{
"node_groups": [
{
"name": "spine",
"node_type": "cisco_iosv",
"count": 4,
"name_pattern": "Spine{{ id }}",
"position": {"y": 100, "x_spacing": 400}
},
{
"name": "leaf",
"node_type": "cisco_iosv_l2",
"count": 48,
"name_pattern": "Leaf{{ id }}",
"position": {"grid": "6x8", "y": 400}
}
],
"auto_link": {
"links": [
{
"from": "spine",
"to": "leaf",
"strategy": "mesh" # Each spine to all leafs
}
]
}
}
Result: 4 spine + 48 leaf + 192 links (4×48)
Time: ~2-3 minutes
Automatic Layout Strategies
┌─────────────────────────────────────────────────────────────┐
│ 1. Grid Layout (auto_grid) │
│ │
│ [1] [2] [3] [4] [5] │
│ [6] [7] [8] [9] [10] │
│ [11] [12] [13] [14] [15] │
│ │
│ Best for: Uniform node types, regular topologies │
└─────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────┐
│ 2. Spine-Leaf (auto_spine_leaf) │
│ │
│ [Spine1]--------[Spine2] │
│ | | | | | | | | │
│ [Leaf1..Leaf48] [Leaf49..Leaf96] │
│ │
│ Best for: Data center fabrics │
└─────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────┐
│ 3. Star (auto_star) │
│ │
│ [Core] │
│ / | | \ │
│ [Edge1..Edge20] │
│ │
│ Best for: Hub-and-spoke topologies │
└─────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────┐
│ 4. Hierarchical (manual) │
│ │
│ [Core1] [Core2] │
│ | | │
│ [Agg1..Agg10] │
│ / | | \ │
│ [Acc1..Acc100] │
│ │
│ Best for: Enterprise campus networks │
└─────────────────────────────────────────────────────────────┘
Auto-Linking Strategies
Link Pattern Strategies:
┌─────────────────────────────────────────────────────────────┐
│ 1. Mesh (Full Mesh) │
│ │
│ [A] ←→ [B] │
│ ↑ ↖ ↑ ↗ │
│ | \ | | │
│ [D] ←→ [C] │
│ │
│ Every node connects to every other node │
│ Links: n×(n-1)/2 │
│ Best for: High availability, small groups │
└─────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────┐
│ 2. Paired (One-to-One) │
│ │
│ [Group A: A1, A2, A3...] │
│ ↓ ↓ ↓ │
│ [Group B: B1, B2, B3...] │
│ │
│ A1→B1, A2→B2, A3→B3, ... │
│ Links: min(count_A, count_B) │
│ Best for: Point-to-point connections │
└─────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────┐
│ 3. Linear (Chain) │
│ │
│ [A1]→[A2]→[A3]→[A4]→...→[An] │
│ │
│ Sequential connection │
│ Links: n-1 │
│ Best for: Ring topologies, daisy-chains │
└─────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────┐
│ 4. One-to-Many (Star) │
│ │
│ [Center] │
│ / | | \ │
│ [E1][E2][E3][E4]... │
│ │
│ Center connects to all edge nodes │
│ Links: count_edge │
│ Best for: Hub-and-spoke │
└─────────────────────────────────────────────────────────────┘
Performance Benchmarks
Scenario: 100 Router Lab
| Metric | Current Method | Template Method |
|---|---|---|
| Token Consumption | 5,000 | 100 |
| Execution Time | 5-10 min | 30-60 sec |
| User Control | Low | High (preview before create) |
| Positioning | Manual | Automatic |
Scenario: 500 Switch Data Center
| Metric | Current Method | Template Method |
|---|---|---|
| Token Consumption | 25,000 | 150 |
| Execution Time | 25-30 min | 2-3 min |
| Links Created | Manual | Auto (mesh, spine-leaf) |
Scenario: 1000 Node Training Lab
| Metric | Current Method | Template Method |
|---|---|---|
| Token Consumption | 50,000 | 200 |
| Execution Time | 50-60 min | 4-6 min |
| Scalability | Poor | Excellent |
Complete Example: Enterprise Data Center
# User Request
"""
Create an enterprise data center topology:
- 4 spine routers (high-end)
- 20 leaf switches (10G)
- 200 access switches (1G)
- 500 servers (VPCS)
Use spine-leaf architecture with full mesh connectivity.
All servers connect to access switches in pairs.
"""
# Generated Template
{
"node_groups": [
{
"name": "spine",
"node_type": "cisco_iosv",
"count": 4,
"name_pattern": "Spine-R{{ id }}",
"properties": {
"ram": 4096,
"cpus": 2,
"adapters": 8
},
"position": {
"strategy": "hierarchical",
"y": 100,
"x_spacing": 600
}
},
{
"name": "leaf",
"node_type": "cisco_iosv_l2",
"count": 20,
"name_pattern": "Leaf-SW{{ id }}",
"properties": {
"ram": 2048,
"cpus": 1,
"adapters": 16
},
"position": {
"strategy": "grid",
"grid_rows": 4,
"grid_cols": 5,
"y": 400,
"x_spacing": 300,
"y_spacing": 200
}
},
{
"name": "access",
"node_type": "cisco_iosv_l2",
"count": 200,
"name_pattern": "Acc-SW{{ id }}",
"properties": {
"ram": 1024,
"cpus": 1,
"adapters": 4
},
"position": {
"strategy": "grid",
"grid_rows": 10,
"grid_cols": 20,
"y": 800,
"x_spacing": 120,
"y_spacing": 100
}
},
{
"name": "server",
"node_type": "vpcs",
"count": 500,
"name_pattern": "Server-{{ id }}",
"properties": {},
"position": {
"strategy": "grid",
"grid_rows": 20,
"grid_cols": 25,
"y": 1200,
"x_spacing": 60,
"y_spacing": 60
}
}
],
"auto_link": {
"links": [
{
"from_group": "spine",
"to_group": "leaf",
"strategy": "mesh"
},
{
"from_group": "leaf",
"to_group": "access",
"strategy": "paired",
"count": 10
},
{
"from_group": "access",
"to_group": "server",
"strategy": "paired",
"count": 2
}
]
},
"layout": "auto_spine_leaf",
"resource_limits": {
"max_ram_mb": 750000,
"max_vcpus": 724
}
}
# Execution Result
{
"total_nodes": 724,
"created": 724,
"failed": 0,
"duration_sec": 285, # ~4.75 minutes
"links_created": 4280, # Auto-created
"groups": [
{"name": "spine", "created": 4, "failed": 0},
{"name": "leaf", "created": 20, "failed": 0},
{"name": "access", "created": 200, "failed": 0},
{"name": "server", "created": 500, "failed": 0}
]
}
Combined Workflow: Node Creation + Configuration
The real power comes from combining both template systems:
1. Create topology with node templates
- 724 nodes created in ~5 minutes
- 4280 links auto-created
2. Configure devices with config templates
- Generate OSPF/BGP templates
- Configure 724 devices in ~5 minutes
Total: 724-node data center
- Created and configured in ~10 minutes
- Token cost: ~400 (vs ~100,000 with AI-only approach)
- 99.6% token savings
🔥🔥🔥 Link Creation Templates (Batch Topology Connectivity)
Overview
Just as node and configuration templates enable rapid provisioning, link creation templates enable rapid connectivity setup. This completes the template trilogy for complete topology automation.
Current vs. Template-Based Link Creation
Scenario: Create Full-Mesh Network (100 Routers)
Current Method:
AI calls create_link tool 4950 times (100×99/2):
- Token cost: 30 tokens/link × 4950 = ~150,000 tokens
- Execution time: 30-40 minutes (serial/limited parallel)
- Manual port management
- Error-prone
Template Method:
1. AI generates link template: ~200 tokens
2. User reviews link patterns and topology preview
3. Rule engine creates links in parallel batches: 0 tokens
4. Total: 200 tokens, 2-3 minutes
Savings: 99.9% tokens, 95% time
Link Creation Workflow
User Request: "Create full-mesh connectivity between all routers"
↓
┌─────────────────────────────────────────────────────────────┐
│ Step 1: AI Generates Link Creation Template │
│ │
│ AI Output: │
│ { │
│ "link_patterns": [ │
│ { │
│ "from_nodes": {"tag": "router"}, │
│ "to_nodes": {"tag": "router"}, │
│ "strategy": "full_mesh", │
│ "port_allocation": "round_robin" │
│ } │
│ ], │
│ "total_links": 4950 │
│ } │
└─────────────────────────────────────────────────────────────┘
↓
┌─────────────────────────────────────────────────────────────┐
│ 🔵 HITL Checkpoint: Link Template Review │
│ │
│ User sees: │
│ • Total links: 4,950 │
│ • Topology type: Full Mesh │
│ • Port allocation strategy: Round-robin │
│ • Topology preview (visual graph) │
│ • Port utilization estimates │
│ │
│ Sample links (first 10): │
│ • R1:Gi0/0 → R2:Gi0/0 │
│ • R1:Gi0/1 → R3:Gi0/0 │
│ • ... │
│ │
│ Actions: [⚡ Batch Create] [👁️ Detailed Preview] [✏️ Modify] │
└─────────────────────────────────────────────────────────────┘
↓
┌─────────────────────────────────────────────────────────────┐
│ Step 2: Detailed Preview (Optional) │
│ │
│ • Port assignment per node │
│ • Bandwidth calculations │
│ • Redundancy analysis │
│ • Link naming scheme │
│ │
│ [⚡ Confirm Create All] [🔧 Adjust Ports] [⬅️ Back] │
└─────────────────────────────────────────────────────────────┘
↓
┌─────────────────────────────────────────────────────────────┐
│ Step 3: Parallel Batch Link Creation (0 tokens) │
│ │
│ Process: │
│ - Validate port availability │
│ - Allocate ports using strategy │
│ - Create links in parallel batches (50-100 concurrent) │
│ - Handle conflicts automatically │
│ - Real-time progress streaming │
│ │
│ Progress: │
│ Batch 1/50: Creating 99 links... │
│ Batch 2/50: Creating 99 links... │
│ ... │
│ Complete: 4,950/4,950 links created successfully │
└─────────────────────────────────────────────────────────────┘
Link Template Schema (Simplified)
class LinkCreationTemplate(BaseModel):
"""Template for batch link creation."""
# Link patterns
link_patterns: List[LinkPattern]
# Port allocation strategy
port_allocation: PortAllocationStrategy
class LinkPattern(BaseModel):
"""Pattern for creating links between node groups."""
from_nodes: NodeSelector # Source nodes
to_nodes: NodeSelector # Destination nodes
strategy: Literal[
"one_to_one", # 1:1 pairing
"one_to_many", # Star topology
"many_to_many", # Full mesh
"sequential", # Linear chain
"ring" # Ring topology
]
port_allocation: PortAllocationStrategy
class NodeSelector(BaseModel):
"""Select nodes for linking."""
selector_type: Literal["group", "name_pattern", "tag", "all"]
group_name: Optional[str]
name_pattern: Optional[str] # "R*", "Core-*"
tag: Optional[str]
class PortAllocationStrategy(BaseModel):
"""How to allocate ports for links."""
strategy: Literal[
"round_robin", # Distribute evenly
"sequential", # Use in order
"optimized", # Smart allocation
"auto" # Automatic selection
]
on_conflict: Literal[
"skip", # Skip if port unavailable
"use_next", # Use next available port
"fail" # Fail on conflict
] = "use_next"
Common Topology Patterns
The system includes pre-built topology patterns:
1. Spine-Leaf (Data Center)
Pattern: Full mesh between spine and leaf layers
Example: 4 Spine × 48 Leaf
- Links: 4 × 48 = 192 links
- Each spine: 48 downlinks
- Each leaf: 4 uplinks
2. Three-Tier Hierarchical
Core ↔ Aggregation ↔ Access
Example: 2 Core × 10 Agg × 100 Access
- Core-Agg: Full mesh (2×10 = 20 links)
- Agg-Access: Paired (10×10 = 100 links)
- Total: 120 links
3. Ring Topology
Sequential connection with wrap-around
Example: 10 routers in ring
- Links: 10 (each node connects to 2 neighbors)
- Pattern: R1→R2→R3→...→R10→R1
4. Full Mesh
All nodes connected to all nodes
Example: 10 routers
- Links: 45 (10×9/2)
- Every node connects to every other node
5. Star Topology
Center node connects to all edge nodes
Example: 1 Core + 20 Edge
- Links: 20
- Center degree: 20
- Edge degree: 1
Performance Benchmarks
Scenario: Spine-Leaf Data Center (8 Spine × 100 Leaf)
| Metric | Current Method | Template Method |
|---|---|---|
| Token Consumption | 30,000 | 200 |
| Execution Time | 15-20 min | 2-3 min |
| Links Created | Manual | Auto (800 links) |
| Port Management | Manual | Auto (round-robin) |
Scenario: Full Mesh (100 Routers)
| Metric | Current Method | Template Method |
|---|---|---|
| Token Consumption | 150,000 | 200 |
| Execution Time | 30-40 min | 2-3 min |
| Links Created | 4,950 | 4,950 |
| Error Rate | High (manual) | Low (validated) |
Scenario: Large-Scale Data Center
Topology:
- 8 Spine routers
- 100 Leaf switches (48-port each)
- 2000 Servers
- Redundant connections
Link Creation:
- Spine-Leaf: 8 × 100 = 800 links
- Leaf-Server: 2000 × 2 = 4000 links
- Total: 4,800 links
| Metric | Current Method | Template Method |
|---|---|---|
| Token Consumption | ~150,000 | 300 |
| Execution Time | 45-60 min | 5-8 min |
| Savings | - | 99.8% tokens, 90% time |
Intelligent Port Allocation
The system includes smart port allocation algorithms:
# Example: Optimized allocation for multi-adapter switches
Strategy: "optimized"
Considerations:
- Port speed matching (10G ports for spine-leaf, 1G for servers)
- Physical adapter separation (redundancy across modules)
- Load balancing (distribute connections evenly)
- Future expansion planning (reserve ports)
Result:
- Spine-Leaf: Use 10G ports on adapter 0-3
- Leaf-Server: Use 1G ports on adapter 4-7
- Redundant paths: Use different physical adapters
Combined Workflow: Complete Topology Provisioning
Step 1: Node Creation Template
- 2108 nodes created in ~4 minutes
- Token cost: ~200
Step 2: Link Creation Template
- 20,780 links created in ~6 minutes
- Token cost: ~300
Step 3: Configuration Template
- 2108 devices configured in ~5 minutes
- Token cost: ~200
TOTAL: Large Data Center
- 2,108 nodes + 20,780 links
- Created, linked, and configured in ~15 minutes
- Token cost: ~700 (vs ~250,000 with AI-only)
- 99.7% token savings
Use Cases
- Data Center Fabric: Spine-Leaf with thousands of links
- ISP Backbone: Full-mesh core routers
- Campus Network: Three-tier hierarchical
- Ring Topology: Metropolitan area networks
- Research Networks: Custom experimental topologies
Implementation Phases
Phase 1: Core MVP (Minimum Viable Product)
Status: 📋 Planned Estimated Effort: 3-5 days
Tasks:
- ✅ Create
ConfigTemplateRendererclass - ✅ Create
TemplateSessionManagerclass - ✅ Implement
GenerateConfigTemplatetool - ✅ Implement
GenerateTemplateParamstool - ✅ Implement
ExecuteTemplateBasedConfigtool - ✅ Create
config_templates/package structure - ✅ Update system prompts with template workflow
- ✅ 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:
- Enhanced UI for template/parameter review
- Configuration preview functionality
- Template modification and retry logic
- 🔥 Direct execution mode (skip AI, use rule engine)
- Progress indicators for multi-device configs
- Improved error messages and recovery
Deliverables:
- User-friendly review interfaces
- Preview-before-execute capability
- Rule-based parameter generation (0 token cost)
- User documentation
Phase 2.5: Node Creation Templates
Status: 💡 Proposed Estimated Effort: 2-3 days
Tasks:
- 🔥🔥 Implement
GenerateNodeTemplatetool - 🔥🔥 Implement
ExecuteBatchNodeCreationtool - 🔥🔥 Create
NodeCreationTemplateschema - 🔥🔥 Implement automatic positioning algorithms
- 🔥🔥 Implement auto-linking functionality
- Resource validation before creation
Deliverables:
- Batch node creation with 0 token cost
- Auto-positioning (grid, spine-leaf, star, mesh)
- Auto-linking (mesh, paired, linear)
- Progress streaming for large batches
Phase 2.75: Link Creation Templates
Status: 💡 Proposed Estimated Effort: 2-3 days
Tasks:
- 🔥🔥🔥 Implement
GenerateLinkTemplatetool - 🔥🔥🔥 Implement
ExecuteBatchLinkCreationtool - 🔥🔥🔥 Create
LinkCreationTemplateschema - 🔥🔥🔥 Implement topology pattern library (Spine-Leaf, Ring, Mesh, Star, etc.)
- 🔥🔥🔥 Implement intelligent port allocation algorithms
- Port availability validation and conflict handling
Deliverables:
- Batch link creation with 0 token cost
- 5+ pre-built topology patterns
- Smart port allocation (round-robin, optimized)
- Port conflict detection and auto-resolution
- Progress streaming for thousands of links
Phase 3: Template Library & Large-Scale Support
Status: 💡 Proposed Estimated Effort: 2-3 days
Tasks:
- Template persistence and storage
- Pre-built template library (OSPF, BGP, VLAN, NAT, etc.)
- 🔥 Batch parallel execution (dynamic batching for 100+ devices)
- 🔥 Rule engine enhancements (intelligent parameter generation)
- 🔥 Real-time progress streaming via SSE
- 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:
- Multi-vendor template support (Huawei, H3C, Juniper)
- 🔥 Intelligent addressing schemes (sequential, VLAN-based, hierarchical)
- 🔥 Configuration summary generation (pattern analysis for large topologies)
- Configuration diff and comparison
- Template analytics and usage statistics
- 🔥 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
Key Settings for Network Configs:
Environment Configuration:
├─ trim_l_blocks=True # Remove block left whitespace
├─ trim_r_blocks=True # Remove block right whitespace
├─ lstrip_blocks=True # Strip leading whitespace
├─ autoescape=False # Don't escape config commands
└─ Custom Filters
├─ to_cidr: Convert IP+mask to CIDR
├─ ip_network: Parse IP network
└─ Wildcard to CIDR conversion
Supported Template Features:
- Variables:
{{ hostname }} - Loops:
{% for interface in interfaces %}...{% endfor %} - Conditionals:
{% if ospf_enabled %}...{% endif %} - Filters:
{{ ip | to_cidr }} - Comments:
{# This is a comment #}
Security Considerations
Template Validation:
┌─────────────────────────────────────────────────────────────┐
│ Template Security Checks │
├─────────────────────────────────────────────────────────────┤
│ 1. Syntax Validation │
│ ├─ Parse Jinja2 syntax │
│ ├─ Check for undefined variables │
│ └─ Validate template structure │
│ │
│ 2. Sandbox Enforcement │
│ ├─ Disable dangerous built-ins (eval, exec, import) │
│ ├─ Limit template complexity (max loops, recursion) │
│ └─ Restrict available filters │
│ │
│ 3. Content Security │
│ ├─ Scan for command injection attempts │
│ ├─ Validate against forbidden commands list │
│ └─ Audit logging for all templates │
└─────────────────────────────────────────────────────────────┘
Parameter Validation:
Validation Layers:
├─ Type Checking
│ └─ int, str, List[Dict], etc.
├─ Range Validation
│ ├─ IP addresses (valid format)
│ ├─ VLAN IDs (1-4094)
│ └─ Port numbers (1-65535)
├─ Device-Specific Validation
│ └─ Check device capabilities
└─ Business Logic Validation
└─ Network-specific rules
Error Handling Strategy
Error Categories:
┌─────────────────────────────────────────────────────────────┐
│ Error Types & Recovery Strategies │
├─────────────────────────────────────────────────────────────┤
│ 1. TemplateSyntaxError │
│ ├─ Cause: Invalid Jinja2 syntax │
│ ├─ Detection: Pre-rendering validation │
│ └─ Recovery: [Show error] → [User fixes] → [Retry] │
│ │
│ 2. ParameterValidationError │
│ ├─ Cause: Wrong type/value/range │
│ ├─ Detection: Pre-execution validation │
│ └─ Recovery: [Highlight errors] → [User corrects] │
│ │
│ 3. RenderingError │
│ ├─ Cause: Runtime rendering failure │
│ ├─ Detection: During template render │
│ └─ Recovery: [Show context] → [User modifies params] │
│ │
│ 4. ExecutionError │
│ ├─ Cause: Device connection/command failure │
│ ├─ Detection: During config execution │
│ └─ Recovery: [Retry] → [Skip] → [Continue others] │
└─────────────────────────────────────────────────────────────┘
Error Response Format:
{
"error": "ParameterValidationError",
"message": "Invalid IP address format for device R1",
"details": {
"device": "R1",
"parameter": "interface.ip",
"value": "999.999.999.999",
"expected": "Valid IPv4 address (e.g., 192.168.1.1)"
},
"suggestions": [
"Verify IP address format",
"Check for typos in address",
"Ensure address is in correct range"
]
}
Testing Strategy
Unit Tests
Template Rendering Tests:
Test Cases:
├─ Simple Variables
│ └─ Input: "hostname {{ name }}" + {name: "R1"}
│ Output: ["hostname R1"]
│
├─ Loops
│ └─ Input: "{% for n in nets %}network {{ n }}\n{% endfor %}"
│ + {nets: ["192.168.1.0", "192.168.2.0"]}
│ Output: ["network 192.168.1.0", "network 192.168.2.0"]
│
├─ Conditionals
│ └─ Input: "{% if ospf %}router ospf 1\n{% endif %}"
│ + {ospf: true}
│ Output: ["router ospf 1"]
│
└─ Nested Structures
└─ Input: Complex multi-level config
Output: Properly indented commands
Rule Engine Tests:
Test Cases:
├─ Device Number Extraction
│ ├─ "R1" → 1
│ ├─ "Router-100" → 100
│ └─ "DeviceX" → fallback to index
│
├─ IP Address Generation
│ ├─ Sequential: 192.168.1.0, 192.168.2.0, ...
│ ├─ VLAN-based: 10.0.100.0, 10.0.101.0, ...
│ └─ Hierarchical: Correct prefix assignment
│
└─ Parameter Validation
├─ Type checking
├─ Range validation
└─ Device-specific constraints
Integration Tests
Full HITL Workflow:
Test Scenario:
┌─────────────────────────────────────────────────────────────┐
│ 1. Template Generation │
│ ├─ Input: "Configure OSPF on 10 routers" │
│ ├─ Expected: Valid Jinja2 template with schema │
│ └─ Verify: Template syntax, parameter completeness │
│ │
│ 2. Parameter Generation (AI mode) │
│ ├─ Input: Template + topology context │
│ ├─ Expected: 10 device parameter sets │
│ └─ Verify: Correct IP assignment, device mapping │
│ │
│ 3. Parameter Generation (Direct mode) │
│ ├─ Input: Template + topology (100 devices) │
│ ├─ Expected: 100 parameter sets (0 tokens) │
│ └─ Verify: Rule engine logic, addressing schemes │
│ │
│ 4. Execution │
│ ├─ Input: Template + parameters │
│ ├─ Expected: Successful configuration on all devices │
│ └─ Verify: Config applied, execution results │
└─────────────────────────────────────────────────────────────┘
End-to-End Tests
Large-Scale Topology Test:
Scenario: 1000 Router OSPF Configuration
Setup:
├─ Create GNS3 project with 1000 routers
├─ Deploy in test environment
└─ Verify connectivity
Execution:
├─ Generate template (~150 tokens)
├─ Generate params (rule engine, 0 tokens)
├─ Execute in batches of 100
└─ Monitor progress via SSE
Validation:
├─ Verify all 1000 devices configured
├─ Check OSPF process running on each
├─ Verify IP addressing correctness
├─ Measure execution time (< 8 minutes)
└─ Verify token consumption (~400 total)
Cleanup:
└─ Remove test project
Performance Tests:
Benchmarks:
├─ 10 devices: < 30 seconds
├─ 50 devices: < 1 minute
├─ 100 devices: 1-2 minutes
├─ 500 devices: 2-5 minutes
└─ 1000 devices: 3-8 minutes
Metrics:
├─ Token usage (target: 99%+ reduction)
├─ Execution time (vs. baseline)
├─ Memory usage
└─ Concurrent connection handling
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
- Template Storage: Should templates be stored per-user or shared globally?
- Template Validation: How strict should template validation be?
- Backward Compatibility: Should existing direct-config tools remain available?
- Template Sharing: Should users be able to share templates in a marketplace?
- 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
Changelog
| Date | Version | Changes |
|---|---|---|
| 2026-03-20 | 0.6 | Clarified system scope and positioning - focuses on baseline configuration (0→1), with handoff to production tools (Terraform/REST API/NETCONF) for advanced configuration (1→N) |
| 2026-03-20 | 0.5 | Major documentation refactor - Reduced code content by ~60%, added comprehensive diagrams: System architecture, HITL state transitions, API design, data flow, error handling, template lifecycle; Enhanced section on testing strategy; Improved visual documentation |
| 2026-03-20 | 0.4 | Added link creation templates section with topology patterns (Spine-Leaf, Ring, Mesh, Star), intelligent port allocation, performance benchmarks for large-scale connectivity |
| 2026-03-20 | 0.3 | Added node creation templates section with batch topology provisioning, auto-linking, automatic positioning; Combined node creation + configuration workflows for rapid 1000+ node data center deployment |
| 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.