Nautobot: Deep Dive

Nautobot's architecture is modular and extensible, empowering network automation through a set of fundamental, tightly-integrated components. Below are the core elements that form the backbone of Nautobot’s automation ecosystem:

• Database Engine (Source of Truth): Maintains a centralized, authoritative database of network devices, sites, racks, IP addresses, interfaces, and connections. This acts as the source of truth for all network infrastructure data.
• Data Modeling Layer: Provides a flexible schema with pre-built models and the ability to define custom fields, relationships, tags, and validation rules, making it adaptable to diverse organizational requirements.
• REST & GraphQL APIs: Delivers robust programmatic access, enabling integrations with existing tools and facilitating automated workflows via both REST and GraphQL endpoints.
• Job Execution Framework: Allows users to define and execute Python-based automation tasks and workflows (jobs) directly within Nautobot, which can be scheduled, triggered on demand, or integrated into pipelines.
• Plugin System: Supports installing and managing plugins—modular additions that extend Nautobot’s native capabilities with features for compliance, reporting, orchestration, and custom UIs.
• User Interface (UI): Offers a responsive, web-based GUI for interactive data exploration, job management, and administration—all backed by role-based access controls and user authentication.
• GraphQL Data Explorer: Provides an interactive interface for exploring and querying data using GraphQL, allowing advanced users to craft precise queries for complex reporting and integration use cases.
• Change Logging & Data Validation: Tracks all configuration and data changes for auditability, while enforcing validation rules and constraints to ensure consistency and compliance across the network inventory.
• Extensibility & Integrations: Features webhooks, custom scripts, integrations with external systems (e.g., source control, ticketing, monitoring), and APIs for seamless automation and orchestration.
• Deployment Flexibility: Can be deployed as Docker containers, on virtual machines, or traditional servers, supporting a range of operational environments from proof-of-concept labs to production data centers.

Selecting a network automation platform or a source of truth solution is a pivotal architectural decision for IT and network teams. Nautobot, NetBox, and similar contenders all offer extensible open-source frameworks—but have important differences that can shape an organization's automation journey. This comparative analysis highlights the core strengths, contrasting features, and strategic considerations between Nautobot and its most popular competitors, with a primary focus on NetBox.

Summary Table: Nautobot vs. NetBox

Key Comparative Insights

• Automation Focus:
  • Nautobot uniquely offers an integrated Python jobs framework, allowing users to run automation tasks from the UI or API. This goes beyond NetBox, where automation runs externally (Ansible, Nornir, etc.), making Nautobot particularly valuable for teams ready for event-driven or in-platform workflows.
• GraphQL and APIs:
  • Nautobot supports both REST and GraphQL APIs (including an interactive GraphiQL explorer), supporting modern integration patterns and flexible queries. NetBox, while robust in the REST API domain, does not natively offer GraphQL.
• Plugin & App Ecosystem:
  • Both platforms offer a mature, extensible plugin system. Nautobot’s apps go further with specialized automation, compliance, onboarding, and SSoT capabilities—often with integrated UIs and jobs. NetBox’s ecosystem is larger due to its longer tenure, but Nautobot’s is quickly catching up and often more automation-centric.
• Data Modeling & Validation:
  • Both support custom models and fields, but Nautobot emphasizes dynamic relationships and a data validation engine to enforce business rules ahead of time, helping avoid bad data at entry.
• Community and Growth:
  • NetBox has an established, large user base and widespread adoption in data center and ISP environments. Nautobot, a younger fork focused on extensibility and automation, is rapidly growing and increasingly favored in organizations prioritizing integrated automation workflows.
• Operational Fit:
  • Both platforms cater to hybrid, on-prem, or cloud deployments. Nautobot may be preferred by teams looking for a “one-stop” automation toolset; NetBox remains a mainstay for foundational infrastructure modeling with external automation.

Strategic Recommendations

• Choose Nautobot if:
  • You want in-platform jobs/automation with RBAC control and compliance tracking.
  • GraphQL or advanced API flexibility is required for integrations or dashboards.
  • You plan to leverage a rapidly-expanding app ecosystem for automation, SSoT, or compliance.
• Choose NetBox if:
  • Your primary need is robust, proven infrastructure modeling and IPAM in large-scale environments.
  • You already use established automation tools and want a highly stable source of truth with broad plugin support.
  • An official, long-standing open-source community and a wide pool of user-contributed resources is critical.

Other Notable Alternatives

• Infoblox:
  • Commercial, feature-rich DNS, DHCP, and IPAM (DDI) platform. Strong at large-scale IPAM, integrations, and DNS/DHCP automation, but less open and more costly than Nautobot or NetBox.
• phpIPAM:
  • Open source, focused mainly on IPAM, without the deep extensibility or automation frameworks of Nautobot/NetBox.
• SolarWinds IP Address Manager:
  • Commercial solution, well-integrated with the SolarWinds Orion ecosystem; best for teams needing GUI-heavy asset management and prebuilt reports.

Conclusion

Both Nautobot and NetBox are leading open-source platforms advancing the network source of truth and automation ecosystem. Nautobot will appeal most to teams seeking next-generation automation, compliance, and integration—while NetBox remains a rock-solid option for infrastructure modeling and traditional IPAM at scale. Selecting the right platform depends on your team's automation maturity, existing workflows, and long-term strategic goals.

Nautobot’s flexibility and extensibility power a diverse set of automation and network management scenarios. Below are some of the most impactful and practical use cases for organizations using Nautobot, with real-world examples:

• Centralized Source of Truth for Networking:
  Nautobot models the intended state of your entire network, centralizing device inventories, IP address management, circuit records, and topology information into one authoritative platform.
  Example: Instead of maintaining separate spreadsheets and proprietary tools for data centers, branch offices, and cloud environments, teams can rely on Nautobot to present live, synchronized data for all sites and device types.
• Extensible Data Platform for Automation:
  Nautobot integrates seamlessly with automation tools like Ansible, Nornir, or custom Python scripts, as well as external data sources such as CMDBs and IPAM systems. It enables automated configuration generation, compliance checks, and end-to-end change management.
  Example: Use Nautobot as the inventory source for Ansible playbooks, automatically generating device configs and pushing updates across the network from a single, accurate dataset.
• Network Automation Apps & Plugins:
  Extend Nautobot’s functionality by building and deploying custom apps and plugins for specialized needs. Achieve deep integration or add new features without altering Nautobot's core.
  Example: Deploy the Nautobot Golden Config plugin to enforce configuration standards, or build a custom device onboarding app to automate the provisioning of new network equipment.
• Automated Compliance and Remediation:
  Nautobot can automatically detect configuration drift, security misconfigurations, or policy violations, and trigger remediation actions—ensuring sustained regulatory and operational compliance.
  Example: Integrate Nautobot with a compliance plugin that audits device configs nightly and rolls back changes or alerts the operations team if unauthorized modifications are detected.
• Lifecycle and Asset Management:
  Tracks the entire lifecycle of network devices, including inventory status, warranty periods, end-of-life notifications, and scheduled replacements—supporting proactive asset management.
  Example: Leverage Nautobot to alert stakeholders when a device’s support contract is expiring or to generate upgrade schedules for hardware reaching end-of-life.
• Real-Time Device Monitoring and Management:
  Integrate Nautobot with network monitoring and management tools for instant visibility into device health, connectivity, and change events—supporting rapid troubleshooting and proactive network operations.
  Example: Link Nautobot with monitoring platforms like Prometheus or Grafana to visualize network state and trigger alerts on anomalies, such as link failures or capacity thresholds.
• Integration with DevOps & GitOps Workflows:
  Nautobot can be integrated into CI/CD pipelines and infrastructure-as-code processes, enabling automated, version-controlled network changes and deployments via Git, API calls, or webhooks.
  Example: Automate the deployment of new VLANs or routing changes by merging a pull request in Git, which updates Nautobot as the source of truth and triggers downstream automation jobs.

The Nautobot App Ecosystem empowers users to extend and tailor their network automation platform with a robust suite of apps and plugins. These apps collectively address a diversity of network operational requirements, making Nautobot highly adaptable for organizations with varying use cases. Below, key components and leading apps within the ecosystem are detailed along with practical examples for each:

• Device Onboarding App:
  Simplifies the addition of new devices to Nautobot by automating device profiling, data gathering, and record creation. Rather than manually entering every attribute, users provide minimal information (e.g., device name, site, and IP address), and the app uses protocols like Netmiko and NAPALM to auto-populate model, platform, manufacturer, and management interface details.
  Example: Onboard a new Cisco switch by supplying only its management IP, site, and platform. The app fetches device details via SSH or API and adds the device to Nautobot with accurate, minimal manual effort[6][14].
• Golden Configuration App:
  Provides automated configuration backups, compliance checks, and facilitates the generation and deployment of intended (golden) configurations. The app compares current device configs to predefined templates and can remediate drift.
  Example: Back up all router configurations nightly and compare them to golden templates. If discrepancies or compliance violations are found, notify operators or generate remediation scripts to bring devices into compliance automatically[7][15].
• Single Source of Truth (SSoT) App:
  Enables two-way data sync between Nautobot and other authoritative sources (such as ServiceNow, Infoblox, or Arista CloudVision) through Jobs and a graphical dashboard. It helps unify network data and prevents silos by making Nautobot a clearinghouse for all infrastructure records.
  Example: Automatically import data from Infoblox to update IP address information in Nautobot, or export device inventory to ServiceNow for seamless service management and reporting[8][16].
• ChatOps App:
  Integrates chatbots into platforms like Slack, Microsoft Teams, and WebEx Teams, enabling network engineers to interact with Nautobot directly from chat. Built-in commands facilitate inventory checks, capacity reports, or even triggering automation jobs—all via chat.
  Example: A user types "/nautobot inventory site=san-antonio" in Slack to instantly view device inventory for the San Antonio data center, or checks port status without leaving the chat window[9][17].
• Device Lifecycle Management App:
  Tracks hardware/software lifecycle, end-of-life (EoL) notices, support contracts, and even security vulnerabilities through NIST NVD integration. Provides dashboards and scheduled reporting to streamline planning for upgrades and replacement.
  Example: Generate a report of all switches reaching EoL in the next quarter, view affected devices, contract status, and related security vulnerabilities in one interface[10][18].
• Firewall Models App:
  Standardizes the modeling and management of firewall policies and rule objects using a vendor-agnostic, data-driven approach. Supports multi-vendor policies and advanced assignment logic based on dynamic device groups.
  Example: Design a campus-wide firewall access policy within Nautobot, then export standardized rules to both Palo Alto and Cisco firewalls using the same app-driven model[11][19].
• Data Validation Engine:
  Enables organizations to build and enforce custom validation and compliance rules for network data using user-definable logic (regular expressions, required fields, uniqueness checks, etc.). Ensures consistent, high-quality data across all records.
  Example: Enforce device naming conventions, validate interface descriptions, and audit all existing device records for compliance with internal company standards, producing compliance status and detailed error reports[12][20].
• Nornir Plugin:
  Extends Nautobot's automation capabilities, integrating the Nornir automation framework to execute tasks and workflows with device inventory and credentials managed directly from Nautobot. The plugin is used by other apps (like Golden Config) to run distributed automation jobs.
  Example: Run a firmware upgrade task across all routers in a site, leveraging Nornir jobs scheduled and tracked from Nautobot's UI, using credentials secured in Nautobot[7].

The Nautobot Jobs framework is a cornerstone of Nautobot’s extensible automation platform. It enables organizations to define, execute, and manage operational tasks—making network automation accessible, auditable, and repeatable. The following elaborates on key features and use cases, providing examples for clarity:

• Python-Based Automation:
  Jobs are developed in Python, letting engineers implement network audits, configuration pushes, inventory validation, and much more. By using Python, users can tap into existing libraries and frameworks.
  Example: Write a job to cross-check all interface descriptions against standardized naming conventions, reporting any discrepancies for remediation.
• UI and API Execution:
  Jobs may be executed through the Nautobot web interface for manual runs, or triggered via the REST API to enable automation within larger network workflows and pipelines.
  Example: A network engineer launches a VLAN deployment job from the UI; the same job can be triggered automatically after a GitOps merge via the API to ensure deployments are consistent and repeatable.
• Custom Inputs and Parameters:
  Each job can present custom forms for user input, such as selecting device roles, sites, or providing configuration snippets. This allows tailored task execution.
  Example: Prompt for a list of device hostnames and a configuration block; push that block only to the selected devices.
• Role-Based Access Control (RBAC):
  Nautobot’s RBAC restricts who can view, schedule, or execute jobs, adding a layer of security—essential for sensitive operations.
  Example: Only users in the "Network Automation Admins" group are allowed to run jobs that make network-wide changes.
• Job Results and Logging:
  Every job run is logged for traceability, including outputs, errors, custom status messages, and detailed context for troubleshooting or audits.
  Example: After a failed configuration deployment, the results log shows the failing device and the returned error message, aiding fast root-cause analysis.
• Scheduling and Event-Driven Automation:
  Jobs can be scheduled for future times or connected to external triggers—enabling regular checks, backups, or remediation routines.
  Example: Schedule a nightly job to back up all switch configurations, or trigger a compliance check job whenever a new device is onboarded.
• Integration with Plugins and Apps:
  Custom jobs can incorporate functions from Nautobot plugins and apps, enabling complex and modular automation that fits evolving network needs.
  Example: Use the Golden Configuration plugin within a job to check network device configs for compliance, then automatically remediate issues or open tickets in ServiceNow.

Ensuring the accuracy, reliability, and accountability of network data is at the core of Nautobot's mission as a network automation platform. Robust data integrity and comprehensive governance features empower organizations to trust their network source of truth, simplify audits, and reduce operational risk. Here’s how Nautobot delivers on these pillars—with practical examples:

• Business Rule Enforcement:
  Nautobot allows administrators to define and enforce custom business logic for all data entries—including mandatory fields, field formats, valid value sets, and naming conventions.
  Example: Require all switches to have location codes in their hostnames and valid serial numbers before records can be saved, preventing accidental or non-compliant entries.
• Change Logging and Audit Trails:
  Every modification in Nautobot is recorded in an immutable change log, capturing the user, timestamp, previous value, new value, and context of the change. This enables end-to-end traceability and supports internal or regulatory audits.
  Example: Easily trace who decommissioned a device, review historical IP address assignments, or investigate the cause and impact of recent configuration changes.
• Data Ownership and Metadata Tracking:
  Nautobot records the source, owner, and current synchronization status of data—essential for environments synchronized with CMDBs, cloud platforms, or external asset tools. This metadata helps resolve data discrepancies and points users to the correct authority.
  Example: Quickly identify which VLANs originated from ServiceNow versus those created directly in Nautobot, making conflict resolution and data stewardship more efficient.
• Validation and Quality Assurance:
  Built-in validations (such as required fields and uniqueness checks) are complemented by custom validation logic that organizations can implement via plugins or the Data Validation Engine.
  Example: Automatically reject device records without valid MAC addresses or flag cables with duplicate serial numbers, ensuring only high-quality data reaches production workflows.
• Role-Based Access Control (RBAC):
  Nautobot implements fine-grained permissions on every major object and action, controlling which users and groups can view, create, update, or delete data. RBAC is crucial for securing sensitive infrastructure and enforcing data governance at scale.
  Example: Only allow senior engineers to delete device records or modify core site attributes, while letting Tier-1 support staff update port descriptions or inventory statuses.
• Integration with External Systems:
  Nautobot synchronizes with external platforms (like CMDBs, Infoblox, or ticketing systems) via plugins, APIs, or webhooks—maintaining data consistency, rich context, and up-to-date records across IT operations.
  Example: Align device lifecycle status in Nautobot with your organization’s asset management database. When a device is marked as decommissioned externally, it’s automatically updated in Nautobot, reducing manual effort and misalignment.

Nautobot is built for operational flexibility, allowing organizations to tailor both deployment and integration to their unique environments. Below, discover how Nautobot fits into a variety of IT landscapes and complements modern automation workflows—with detailed examples:

• Flexible Deployment Options:
  Nautobot can be deployed as a Docker container, on virtual machines (VMs), or on bare-metal servers—adaptable to any infrastructure, from lab environments to production data centers.
  Example: Rapidly spin up a test Nautobot instance with Docker Compose on a laptop for development, then migrate to a production-ready Kubernetes cluster as the deployment scales.
• Cloud and On-Premises Support:
  Whether operating entirely on-premises, fully in the cloud (AWS, Azure, GCP), or using a hybrid/multi-cloud model, Nautobot fits seamlessly into your network topology.
  Example: Deploy Nautobot on AWS EC2 to manage cloud-based networking, while also connecting it to on-prem Edge or branch devices over a secure VPN.
• Native Git Integration:
  Nautobot can clone, read from, and write to Git repositories, enabling Infrastructure-as-Code (IaC) practices, change reviews, and rollbacks directly from your preferred VCS.
  Example: Store golden configuration templates for routers in a GitHub repo—Nautobot pulls updates and applies them across your fleet after management approval.
• API-First Architecture:
  Powerful REST and GraphQL APIs provide deep programmatic access, allowing external systems, automation scripts, and monitoring tools to interact with Nautobot seamlessly.
  Example: Integrate Ansible playbooks or custom Python scripts to automatically update device inventory in Nautobot whenever new equipment is deployed.
• CI/CD and DevOps Toolchain Integration:
  Nautobot is easily integrated into pipelines with CI/CD tools such as Jenkins, GitLab CI, or GitHub Actions—automating the testing and deployment of network changes.
  Example: A pull request in Git triggers a pipeline that validates intended VLAN changes via Nautobot’s API and deploys changes automatically after pipeline approval.
• Plugin and App Ecosystem:
  Augment Nautobot’s core with community-contributed or custom plugins—enabling integration with systems like ServiceNow (ITSM), Infoblox (IPAM), Slack (ChatOps), and more.
  Example: Use the Nautobot SSoT (Single Source of Truth) plugin to synchronize asset and IP address data with your ServiceNow CMDB, keeping all records current.
• Authentication and Access Control:
  Seamlessly integrate with enterprise identity providers via LDAP, SSO (SAML or OIDC), or OAuth2, ensuring secure, scalable, and centrally managed user access.
  Example: Enforce single sign-on for network engineers using Microsoft Azure AD, giving users easy access while administrators maintain strict RBAC policies.
• Scalability and High Availability:
  Nautobot supports clustering and horizontal scaling, with options for redundant web and worker nodes, shared database backends, and load balancing.
  Example: Run Nautobot behind an NGINX load balancer with multiple container instances and a managed PostgreSQL service to deliver 24/7 uptime and handle thousands of network devices.

Choosing the appropriate deployment method is the first step toward a successful Nautobot installation. Nautobot supports multiple deployment options, each suited to different scenarios—from local testing to large-scale, production-grade automation. Here’s a detailed guide to help you decide and implement the right choice:

1. 1. Evaluate Your Requirements
   • Purpose: Are you doing a quick evaluation, development, or planning for production?
   • Resource Availability: Consider server resources, network access, and backup needs.
   • Scalability/Redundancy: Assess if HA, clustering, or cloud-readiness is needed.
2. 2. Choose a Deployment Type
   • Docker Compose: Quickest path for evaluation, trials, and development.
   • Virtual Machine (VM): Suitable if you’re familiar with managing Linux servers and want direct system control.
   • Bare-Metal: Best for maximum performance and complete control in dedicated environments.
   • Kubernetes/Container Orchestration: Recommended for large-scale, cloud-native, or HA production use.
3. 3. Prepare Your Environment
   • For Docker Compose:
     • Install Docker and docker-compose on your host.
     • Ensure ports 8080 (web), 5432 (PostgreSQL), and 6379 (Redis) are available.
   • For VM or Bare Metal:
     • Provision a Linux VM (Ubuntu or CentOS recommended) or a dedicated server.
     • Allocate recommended hardware: 2+ CPUs, 4GB+ RAM, and 20GB+ Disk.
     • Access as a user with sudo privileges.
   • For Kubernetes:
     • Provision a Kubernetes cluster (self-hosted or managed, e.g., EKS/GKE/AKS).
     • Install kubectl and configure your kubeconfig context.
4. 4. Gather Installation Resources
   • Visit the Nautobot official installation page for your chosen method.
   • Download any required sample docker-compose.yml or Kubernetes manifests from the Nautobot GitHub repository or documentation.
5. 5. Execute the Initial Deployment
   • Docker Compose:
     1. Clone the Nautobot Docker deployment repository.
     2. Customize the .env file if needed (database password, admin credentials, etc.).
     3. Run docker-compose up -d to start Nautobot and dependencies.
   • Virtual Machine/Bare Metal:
     1. Install required dependencies (Python 3.8+, PostgreSQL, Redis).
     2. Follow Nautobot’s manual installation steps: clone the repo, set up a Python venv, and install Nautobot with pip.
     3. Configure Gunicorn/UWSGI and a web proxy (e.g., nginx) for serving the app.
   • Kubernetes:
     1. Apply official Nautobot Helm chart or manifests, customizing values for your cluster and secrets.
     2. Configure persistent storage, environment variables, and service ingress as needed.
6. 6. Verify Installation
   • Access Nautobot via the web interface (default: http://localhost:8080 or assigned IP/DNS).
   • If installing on VMs/bare metal, check Nautobot and component services are running (systemctl status or docker ps as needed).
   • For containers or Kubernetes, use docker-compose logs or kubectl logs to troubleshoot.
7. 7. Next Steps
   • Proceed to database initialization and superuser creation as described in the next guide step.
   • Optional: Set up SSL/TLS termination, firewall rules, and backups as part of your deployment hardening.

Tip: Start with Docker for trial or labs, and progress to VM/Kubernetes for production or larger environments. Always follow best practices for your chosen infrastructure!

The Nautobot database—typically powered by PostgreSQL—is your network’s authoritative source of truth. Proper initialization is essential to ensure reliable inventory, backup, automation, and scalability.

1. 1. Confirm Database Requirements
   • Nautobot requires PostgreSQL (version 10+ recommended).
   • Tip: Use a dedicated PostgreSQL instance or service for production for better security, performance, and future scaling.
2. 2. Set Up the Database Server
   • For Docker Compose, the provided docker-compose.yml spins up PostgreSQL automatically.
   • For VM/Bare Metal installs, install PostgreSQL using your distro’s package manager:
     sudo apt install postgresql (Ubuntu/Debian)
     sudo yum install postgresql-server (RHEL/CentOS)
3. 3. Configure the Database User and Schema
   • Create a database and user for Nautobot:
     sudo -u postgres psql
CREATE DATABASE nautobot;
CREATE USER nautobot WITH PASSWORD 'your_secure_password';
GRANT ALL PRIVILEGES ON DATABASE nautobot TO nautobot;
   • Record your DB name, user, and password for use in Nautobot’s config.
4. 4. Update Nautobot Configuration
   • Open your Nautobot configuration file (nautobot_config.py or .env).
   • Set database connection parameters:
     • DATABASES = { 'default': { 'NAME': 'nautobot', 'USER': 'nautobot', 'PASSWORD': 'your_secure_password', 'HOST': 'localhost', 'PORT': '', } }
5. 5. Run Database Migrations
   • Navigate to your Nautobot project directory.
   • Run: nautobot-server migrate
   • This sets up all required tables, schemas, and indexes automatically.
   • If using Docker, you can execute: docker-compose exec nautobot nautobot-server migrate
6. 6. Create a Superuser Account
   • Run: nautobot-server createsuperuser
   • Provide a username, email, and password when prompted. This will be your initial admin login for the web UI.
   • On Docker: docker-compose exec nautobot nautobot-server createsuperuser
7. 7. Verify Database and Access
   • Start Nautobot (if not already running).
   • Navigate to the web UI (often http://localhost:8080).
   • Log in with your superuser credentials and confirm the core inventory objects (Devices, Sites, Racks, etc.) display in the interface.
   • Tip: If you encounter errors, review database logs/configurations for connection issues or permission errors.
8. 8. Backup and Maintenance (Recommended)
   • Schedule regular PostgreSQL database backups, e.g., using pg_dump.
   • Monitor database logs and set up alerts for anomalies.
   • For advanced installs, configure replication or HA database options.

Result: Your Nautobot deployment now features a fully-initialized, authoritative database—forming the foundation for all network inventory, automation, and future integrations.

Once Nautobot and its database are running, the web User Interface (UI) is your primary portal for managing network inventory and workflows. Here’s a comprehensive, step-by-step guide to accessing and exploring the Nautobot web UI:

1. 1. Confirm Service Status
   • Ensure that Nautobot’s web service is up and running on your chosen platform (Docker, VM, or bare metal).
   • Docker: Run docker-compose ps and verify the nautobot container status is “Up.”
   • VM/Bare Metal: Use systemctl status nautobot or check your Gunicorn/UWSGI process.
2. 2. Locate the Web UI Address and Port
   • By default, Nautobot listens on http://localhost:8080 if deployed locally.
   • If installed on a VM or server, substitute localhost with your server’s IP address or DNS hostname.
   • On Docker, verify the mapped port in your docker-compose.yml file (default is usually 8080:8080).
3. 3. Open the Web Browser
   • Launch your preferred browser (Chrome, Firefox, Edge, Safari, etc.).
   • Enter the Nautobot address in the address bar:
     http://localhost:8080 or http://your-server-ip:8080
4. 4. Log In with Superuser Credentials
   • At the login screen, enter your superuser username and password configured during initialization.
   • Click "Log In" to access Nautobot’s main dashboard.
5. 5. Perform Initial UI Walkthrough
   • After logging in, explore the left navigation menu for key inventory sections such as Devices, Sites, Racks, VLANs, and IP Addresses.
   • Click each section to view default tables, sample filters, and add/create buttons.
   • Use the top-right user menu to change your password or manage your account.
6. 6. Adjust UI Preferences (Optional)
   • Click your username or settings icon to access preferences.
   • Customize interface elements, themes, or default landing pages as available.
7. 7. Troubleshoot Access Issues (If Necessary)
   • If the UI does not load, confirm that Nautobot is running and check for firewall rules that might block port 8080.
   • Review Nautobot logs for startup errors:
     docker-compose logs nautobot (Docker)
     journalctl -u nautobot or Gunicorn logs (VM/Bare Metal)
   • Ensure your web browser and operating system are not blocking the desired port/address.

Result: You can now access and manage your Nautobot environment via the web interface. All further setup, inventory management, and automation tasks begin from this UI.

The Data Modeling Layer in Nautobot transforms its inventory into a tailored, organization-specific system. By extending the default schemas with custom fields, relationships, tags, and validation rules, you build a network source of truth that truly fits your needs. Here’s how to set up and customize your data model:

1. 1. Review Default Models
   • Navigate to key inventory sections in the Nautobot UI: Devices, Sites, Racks, VLANs, IP Addresses, Circuits, etc.
   • Examine which fields and relationships already exist for each object type—most network attributes (manufacturer, device role, status, site) are present by default.
2. 2. Identify Your Custom Data Needs
   • Consult with stakeholders (e.g., network, security, asset teams) to determine additional data—such as Asset Tag, Purchase Date, Owner, or custom statuses—that you want to track.
   • Document fields or relationships not covered by Nautobot’s defaults.
3. 3. Add Custom Fields
   • In the UI, go to Extensibility → Custom Fields.
   • Click “Add Custom Field” and define:
     • Associated object type (e.g., Device, Site)
     • Field type (text, integer, boolean, URL, etc.)
     • Name, label, defaults, and description
     • Whether the field is required or optional
   • Save and test by viewing/editing the associated objects.
   • Example: Add a custom field "Asset Tag" (text) to all Devices.
4. 4. Define Custom Relationships
   • In the UI, go to Extensibility → Relationships.
   • Click “Add Relationship” to connect two object types (e.g., link Circuits to Providers).
   • Specify direction (one-to-one, one-to-many, many-to-many), labels, and descriptions.
   • Use relationships to connect disparate records in unique ways.
   • Example: Relate devices to their business applications or owners.
5. 5. Apply Tags for Flexible Grouping
   • Use Tags to apply labels for freeform grouping, filtering, and searching.
   • In Organization → Tags, create reusable tags (e.g., “Critical”, “Lab”, “San Antonio”, “Retired”).
   • Tags are assignable to most objects—use them for workflow triggers, reporting, or UI personalization.
6. 6. Set Validation Rules
   • In Extensibility → Validation Rules, create rules to enforce data quality.
   • Define conditions for required fields, naming conventions, regular expressions, and value choices.
   • Example: Enforce device names to match a naming standard (e.g., SITE-ROLE-NUMBER).
7. 7. Test and Refine
   • Edit several inventory records to ensure custom fields, validation, and relationships behave as expected.
   • Refine or expand fields/relationships as your network evolves or new requirements emerge.
8. 8. Document for Your Team
   • Maintain internal documentation on your custom fields, tags, and relationships so all users understand their purpose and proper usage.

Result: Your Data Modeling Layer now efficiently organizes both standard and custom network data—empowering clear inventory tracking, automation, and reporting tailored specifically to your organization’s needs.

Nautobot’s API capabilities make it easy to integrate, automate, and query data—from the web, command line, or custom code. This step-by-step guide covers how to access, authenticate, and experiment with both the REST and GraphQL APIs, complete with practical examples.

1. 1. Access Interactive API Documentation
   • After logging into your Nautobot UI, navigate to /api/docs/ for detailed interactive REST API documentation, or /api/ for a full endpoint list.
   • For GraphQL, browse to /graphql/ to use the GraphiQL explorer, which offers autocomplete and inline documentation for available queries and fields.
   • Example: If Nautobot is at http://localhost:8080, visit http://localhost:8080/api/docs/ or http://localhost:8080/graphql/ in your browser.
2. 2. Generate an API Token
   • In the Nautobot UI, click your username (top right) → Profile → API Tokens.
   • Click “+ Add an API Token”, give it a meaningful description, and copy the generated token securely. Use this token for authenticating your API calls[19].
3. 3. Explore the REST API
   • List objects: Use an HTTP client like curl or Postman to query an endpoint.
     curl -H "Authorization: Token YOUR_TOKEN" http://localhost:8080/api/dcim/devices/
   • Retrieve a single object by ID:
     curl -H "Authorization: Token YOUR_TOKEN" http://localhost:8080/api/ipam/ip-addresses/12345/
   • Create a new object:
     curl -X POST -H "Authorization: Token YOUR_TOKEN" -H "Content-Type: application/json" -d '{"name":"core-router"}' http://localhost:8080/api/dcim/devices/
   • Tip: Most objects have endpoints under /api/dcim/, /api/ipam/, etc. Use /api/docs/ to view all available endpoints and required fields[1][2].
4. 4. Try the GraphQL API in the Web UI
   • Open the GraphiQL explorer at /graphql/. This is an in-browser playground to build and run GraphQL queries interactively with autocomplete and documentation panels.
   • Example query:

     query {
       devices(name: "nyc-sw01") {
         name
         interfaces {
           name
           ip_addresses {
             address
           }
         }
       }
     }
               

   • Click “Play” to execute and view hierarchical results tailored to your query[6][9].
5. 5. Run a GraphQL Query via the API (Advanced)
   • Send POST requests to /api/graphql/ with your token and a JSON body.
   • Example using curl:
     curl -X POST http://localhost:8080/api/graphql/ \ -H "Authorization: Token YOUR_TOKEN" \ -H "Content-Type: application/json" \ -d '{"query": "query { devices { name } }"}'
   • Responses include only requested fields, ideal for powering apps or dashboards[15][12].
6. 6. Reference Query Examples and Resources
   • For REST API: test GET, POST, PATCH, and DELETE requests using your preferred language or tools; find common examples in the Nautobot docs[1][3][4].
   • For GraphQL: use the GraphiQL explorer’s schema panel to discover new queries and variables; review example scripts in blog posts or sample code[6][12].
7. 7. Best Practices
   • Always safeguard your API tokens and use HTTPS in production.
   • For automation, prefer creating service accounts with minimal permissions.
   • Use filtering/query parameters for efficiency (e.g., ?name=core-router on REST endpoints).
   • Monitor API rate limits and usage to maintain performance.

Result: You are now equipped to interact with Nautobot’s powerful REST and GraphQL APIs for data retrieval, automation, and external integration. Continue building scripts, dashboards, and automation workflows tailored to your network’s unique needs.

Nautobot’s Jobs framework empowers you to automate network tasks directly within the platform using Python. Here is a comprehensive, step-by-step guide to creating and running your first custom Job:

1. 1. Understand Nautobot Jobs
   • Jobs are Python scripts securely run within Nautobot to automate tasks ranging from audits to configuration changes.
   • Jobs can take inputs, enforce RBAC permissions, and provide detailed logging and outputs.
2. 2. Prepare the Jobs Directory
   • Jobs are stored in the jobs/ directory inside your Nautobot project or in an app-specific path for plugin-based jobs.
   • Example path: /opt/nautobot/jobs/
   • Ensure this path exists and Nautobot has access.
3. 3. Write Your First Job File
   • Create a new Python file, for example, jobs/device_audit.py.
   • Insert a basic Job class; for example:

     from nautobot.core.jobs import Job
     
     class DeviceAuditJob(Job):
         """Audit all network devices for missing serial numbers."""
     
         class Meta:
             name = "Device Audit"
     
         def run(self, data, commit):
             missing = []
             for device in self.job_model.objects.all():
                 if not device.serial:
                     missing.append(device.name)
             if missing:
                 self.log_warning(f"Devices missing serials: {', '.join(missing)}")
                 return f"Found {len(missing)} devices missing serial numbers."
             return "All devices have serial numbers."
               

   • Tip: Download or copy sample Jobs from Nautobot’s documentation for more complex use cases.
4. 4. Register Jobs in Nautobot
   • Ensure your job modules are referenced in Nautobot’s settings:
     JOBS = [ "/opt/nautobot/jobs/", ]
   • Restart Nautobot to load the new Job.
5. 5. Configure RBAC (Access Control)
   • Navigate to Admin → Permissions.
   • Create or adjust a permission rule to allow your user or group to execute Jobs.
   • Assign only trusted users since jobs can perform sensitive actions on network devices.
6. 6. Run the Job in the UI
   • Go to Extensibility → Jobs in the Nautobot UI menu.
   • Locate “Device Audit” (or your custom job name) in the list.
   • Click “Run Job” to launch it. Fill in any prompted fields if parameters were declared in your job’s code.
   • Click “Submit”. The job executes, and live output/logs are shown in the UI.
   • Review results: warnings, success messages, and the full log are all stored for later audit.
7. 7. Advanced: Schedule or Trigger via API
   • Jobs can be scheduled for future execution within the UI.
   • For automation, use Nautobot’s REST API to trigger jobs programmatically, integrating with CI/CD, scripts, or external systems.

Result: You have now written, registered, and successfully executed your first Nautobot Job—establishing a foundation for automated audits, network changes, and more complex workflows in your environment.

Nautobot plugins extend the platform’s power—from configuration compliance and device onboarding to advanced visualization. Here’s a clear step-by-step guide, using popular plugins (like Golden Config or Device Onboarding) as examples. Adjust the package name and configurations for your chosen plugin.

1. 1. Choose and Review the Plugin
   • Pick a plugin that suits your needs (e.g., nautobot-golden-config, nautobot-device-onboarding, etc.).
   • Read the plugin’s documentation for version compatibility, prerequisites, and any required dependencies.
2. 2. Activate Nautobot's Python Environment
   • If using a virtual environment or a dedicated nautobot user, activate it before installing packages. Example:
   • source /opt/nautobot/venv/bin/activate
     or sudo -iu nautobot
3. 3. Install the Plugin Packages
   • Use pip to install the plugin (replace nautobot-golden-config with your plugin’s name):
   • pip install nautobot-golden-config
   • For persistent installs, append the package to local_requirements.txt (if present):
   • echo nautobot-golden-config >> local_requirements.txt
4. 4. Enable the Plugin in nautobot_config.py
   • Edit your Nautobot configuration file (usually nautobot_config.py, found in your project root).
   • Find or create the PLUGINS list and add the plugin's Python package name:

   • PLUGINS = [
         "nautobot_golden_config",
         # (other plugins)
     ]
     

   • If required, set plugin-specific configuration in PLUGINS_CONFIG (refer to the plugin's docs for options). Example:

     PLUGINS_CONFIG = {
         "nautobot_golden_config": {
             "enable_backup": True,
             "enable_compliance": True,
             # Other options...
         }
     }
     

5. 5. Run Post-Installation Commands
   • Make sure all database migrations and static file operations are completed:
   • nautobot-server post_upgrade
   • For Docker-based installs, use:
   • docker-compose exec nautobot nautobot-server post_upgrade
6. 6. Restart Nautobot Services
   • Restart Nautobot so the plugin is loaded:
   • sudo systemctl restart nautobot nautobot-worker
     (or restart containers if using Docker)
7. 7. Verify Plugin Installation
   • Log into Nautobot’s web UI.
   • Navigate to Plugins or Apps sections—your installed plugin should now appear in the UI.
   • Check for plugin-specific navigation items, dashboards, or settings panels.
8. 8. Test and Configure as Needed
   • Follow any post-install steps from the plugin documentation (e.g., initializing models, adding API credentials, etc.).
   • Run a basic workflow or demo task, such as a configuration compliance scan or device onboarding, to confirm the plugin is functional.

Example: Installing the "Golden Config" plugin:
pip install nautobot-golden-config
echo nautobot-golden-config >> local_requirements.txt
# Edit nautobot_config.py: add "nautobot_golden_config" to PLUGINS, add settings
nautobot-server post_upgrade
sudo systemctl restart nautobot nautobot-worker


Result: Your Nautobot system is now extended with powerful new features via a plugin, ready for specialized automation, compliance, or integration tasks.

Nautobot automatically logs all changes to its objects, and also lets you configure robust data validation. This ensures accountability (who changed what and when) and high-quality, compliant network data. Here’s how to review, customize, and use change logging and data validation:

1. 1. Understand Change Logging in Nautobot
   • Every create, update, or delete operation on key objects is automatically recorded in Nautobot’s built-in changelog, including:
     • What changed (object, fields, before/after values)
     • Who made the change (user and source)
     • When the change occurred (timestamp)
     • Change type (create/update/delete)
   • No additional setup is required for basic object change tracking.
2. 2. View Change Logs in the UI
   • Navigate to any object (e.g., a Device, Site, or VLAN) in the UI.
   • Click the “Changelog” tab for that object to see its full edit history.
   • Tip: Site-wide change logs can be seen under Admin → Change Log for an overview of all recent activity.
   • Review details, revert data if needed, or investigate who made changes for auditing and troubleshooting.
3. 3. Audit Changes for Compliance
   • Use the changelog to create custom reports or alerts—e.g., review all device deletions in the last week or see who last modified site details.
   • Export change logs as CSV or through the REST API for integration with external tools.
4. 4. Set Up Data Validation Rules (via Validation Engine)
   • Go to Extensibility → Validation Rules in the Nautobot UI.
   • Click “Add Validation Rule” and specify:
     • Model/object type (e.g., Device, Interface)
     • When to enforce (on create, update, or both)
     • Field conditions, regular expressions, required values, custom logic
     • Custom error messages for users when validation fails
   • Example: Require all Devices to have a serial number on creation. Add a validation rule where Device.serial is “required.”
5. 5. Test Your Data Validation
   • Attempt to create or edit an object in the UI that violates your new rule (e.g., omit the serial number on a Device).
   • Nautobot will reject the change and display your custom error message, preventing incomplete or non-compliant data entry.
   • Check the error message to ensure it is user-friendly and correctly guides remediation.
6. 6. Advanced: Use Custom Validation Plugins or Scripts
   • For more complex requirements (cross-object checks, multi-field logic, etc.), develop plugins or validation scripts using Python.
   • Example: Enforce that all devices in a given site use IP addresses from a specific range by writing a custom validation plugin.
   • Register your validation logic so it runs on create/update events.
7. 7. Monitor and Refine Your Policies
   • Periodically review the changelog and validation failure tracking to spot patterns or recurring issues.
   • Tweak validation rules or add new ones as requirements evolve or as you notice data quality gaps.

Result: You now have robust change tracking and effective data validation rules in Nautobot, ensuring your network source of truth remains accurate, auditable, and compliant with organizational standards.

Nautobot offers powerful integration and extensibility tools, allowing you to connect with ITSM platforms, trigger external automations, and expand orchestration directly from your network source of truth. This step-by-step guide explains how to set up webhooks, custom scripts, and external integrations—empowering you to automate and streamline network operations.

1. 1. Identify Desired Integrations or Automations
   • Define what external systems or workflows you'd like Nautobot to interact with. Common options include:
     • ITSM systems (e.g., ServiceNow, Jira)
     • ChatOps platforms (Slack, Microsoft Teams)
     • Network monitoring or IPAM tools (Infoblox, SolarWinds, Prometheus)
     • Continuous Integration/Continuous Deployment (CI/CD) tools
2. 2. Enable and Configure Webhooks
   • In Nautobot, go to Extensibility → Webhooks.
   • Click “Add” to create a new webhook:
     • Choose the event/object type (e.g., Device created, IP updated).
     • Enter the target URL (your external system's endpoint or service).
     • Choose events to trigger on (create, update, delete).
     • Decide if authentication, headers, or custom payload templates are needed.
   • Example: Post a Slack message or trigger a CI/CD pipeline whenever a new device enters "production" status.
3. 3. Use Custom Scripts for On-Demand Actions
   • Develop a custom script (Python) that can be launched from the Nautobot UI to perform tasks or interact with external APIs.
   • Place the script in your scripts/ directory, define inputs (device list, site, etc.), and use Python requests or other libraries for integrations.
   • Example: Write a script that bulk-reserves IP addresses in Infoblox based on Nautobot subnet data.
   • Access Extensibility → Scripts to run and monitor outputs.
4. 4. Integrate with External APIs via Plugins or Built-in API
   • For advanced/extensive integration, use official or community plugins:
     • nautobot-ssot: bi-directional sync with systems like ServiceNow and Infoblox.
     • nautobot-chatops: connect Nautobot with chat platforms for natural language automation.
   • Install and configure the plugin as described in the plugin installation guide.
   • Example: Use the SSoT plugin to keep site and device inventory aligned between Nautobot and an external CMDB.
5. 5. Use Nautobot’s REST & GraphQL APIs for External Automation
   • Connect automation tools (e.g., Ansible, Python scripts) or external platforms to Nautobot’s APIs:
     • Authenticate via API token.
     • Example with curl: curl -H "Authorization: Token YOUR_TOKEN" http://localhost:8080/api/dcim/devices/
     • Example with Python (requests):

       import requests
       headers = {"Authorization": "Token YOUR_TOKEN"}
       r = requests.get("http://localhost:8080/api/dcim/sites/", headers=headers)
       print(r.json())
                     

   • Automate bulk updates, data synchronization, or trigger workflows directly from external tools.
6. 6. Validate and Monitor Integrations
   • Test webhooks and scripts by performing sample actions (e.g., add a device, update a site) and verify the external system receives the events or data.
   • Review Admin → Logs or plugin-specific dashboards for error tracking and audit trails.
   • Adjust configuration or error handling if issues are observed.
7. 7. Refine and Expand Automations
   • As your team grows comfortable, stack multiple integrations—for example, triggering a webhook to a workflow engine, which then calls a script, and posts results back to Slack.
   • Use Nautobot’s extensibility to create fully automated, closed-loop processes (e.g., automatically remediate device compliance violations).

Result: Nautobot is now connected with your broader IT and automation ecosystem, able to send and receive data, automate network operations, and extend workflow orchestration both reactively (via webhooks/plugins) and proactively (via custom scripts and API-driven integrations).

As your organization’s network automation needs expand, preparing Nautobot for growth ensures a reliable, scalable, and secure deployment. This proactive setup promotes high availability, data protection, and future extensibility. Follow these step-by-step instructions to future-proof your Nautobot environment:

1. 1. Implement Regular Database Backups
   • Schedule automated backups of your PostgreSQL database using tools like pg_dump or cloud-native backup services.
   • Store backups securely on separate infrastructure or cloud storage.
   • Example: Set a daily cron job to export the database to AWS S3 or an NFS share.
2. 2. Plan for High Availability (HA)
   • Deploy Nautobot and its dependencies (database, Redis) in a redundant fashion:
     • Use container orchestration (Kubernetes, Docker Swarm) or configure failover for VMs.
     • Set up database replication (e.g., PostgreSQL streaming replication or managed cloud services with HA).
   • Example: Run multiple Nautobot and worker instances behind a network load balancer, and set up a multi-AZ PostgreSQL cluster.
3. 3. Enable Monitoring and Alerting
   • Integrate monitoring solutions (Prometheus, Grafana, Nagios, Datadog) to track service health, database status, and system resource usage.
   • Configure alerting for critical events like failed backups, high latency, or application downtime.
   • Example: Use Prometheus exporters to monitor Nautobot and PostgreSQL metrics, and send alerts to Slack or email.
4. 4. Plan for Scaling Resources
   • Assess expected growth in device count, users, and automation tasks.
   • Allocate additional CPU, RAM, and storage to the database and Nautobot instances as needed.
   • Example: When onboarding new sites or hundreds of devices, increase VM specs or scale horizontally using container replicas.
5. 5. Harden Security and Access Controls
   • Enforce RBAC policies, requiring strong authentication (SSO, LDAP, OAuth) and least-privilege permissions for users and service accounts.
   • Regularly audit access logs and review users/groups for appropriateness.
   • Example: Integrate SSO with your corporate IdP and require MFA for admin accounts.
6. 6. Document Your Architecture and Procedures
   • Keep clear documentation of Nautobot’s deployment architecture, backup/restore instructions, scaling playbooks, and support contacts.
   • Version-control your infrastructure-as-code and configuration.
   • Example: Maintain a runbook with steps to restore Nautobot from backup in a disaster scenario.
7. 7. Test DR (Disaster Recovery) Scenarios
   • Periodically simulate failure and recovery—restore the database from backup, fail over to secondary instances, and check application integrity.
   • Address any discovered gaps in your business continuity processes.
   • Example: Once per quarter, restore the latest backup to a test instance and verify application function.
8. 8. Plan for Future Extensibility
   • Review plugin/app ecosystem options periodically for new features, compliance, monitoring, or integration requirements.
   • Use infrastructure-as-code and version control (Ansible, Terraform, Git) to automate deployment changes as you scale.
   • Example: Use GitHub Actions to deploy new plugins or update Nautobot configurations automatically.

Result: By following these steps, your Nautobot deployment will be robust, scalable, secure, and ready to support organizational growth—ensuring reliable network automation for the long term.