There are several excellent Python diagramming packages, but they specialize in different kinds of diagrams. For a document-specialist AI agent, I would install a combination rather than depend on one library.

## Best overall choices

| Package             | Best use                                                         | My rating |
| ------------------- | ---------------------------------------------------------------- | --------: |
| `diagrams`          | Cloud, network, infrastructure, and system architecture          |    9.5/10 |
| `graphviz`          | Flowcharts, dependency maps, decision trees, and directed graphs |    9.5/10 |
| `schemdraw`         | Electrical circuits, engineering diagrams, and flowcharts        |      9/10 |
| `networkx`          | Analyzing and constructing complex networks                      |      9/10 |
| `pyvis`             | Interactive network diagrams in HTML                             |    8.5/10 |
| `matplotlib`        | Custom static technical visuals                                  |      8/10 |
| `plotly`            | Interactive charts and data-driven visuals                       |      9/10 |
| `blockdiag` family  | Sequence, activity, packet, and block diagrams                   |      8/10 |
| `plantuml` wrappers | UML and formal software diagrams                                 |    8.5/10 |
| `erdantic`          | Entity-relationship diagrams from Python models                  |      9/10 |

## 1. Diagrams

Best for:

* AWS architecture
* Azure and GCP architecture
* Kubernetes
* Network infrastructure
* On-premises systems
* Application architecture
* Hybrid-cloud diagrams

```bash
pip install diagrams
```

It uses Graphviz underneath, so Graphviz must also be installed on the operating system.

```python
from diagrams import Cluster, Diagram
from diagrams.aws.compute import EC2
from diagrams.aws.database import RDS
from diagrams.aws.network import ELB

with Diagram("Web Application", show=False):
    load_balancer = ELB("Load Balancer")

    with Cluster("Application Servers"):
        servers = [
            EC2("Server 1"),
            EC2("Server 2")
        ]

    database = RDS("Database")

    load_balancer >> servers >> database
```

Why it is impressive:

* Professional cloud-provider icons
* Simple Python syntax
* Clusters and boundaries
* Automatic layout
* PNG, SVG, and PDF output
* Diagram source can be stored in Git

It supports AWS, Azure, GCP, Kubernetes, Oracle Cloud, on-premises equipment, programming frameworks, SaaS services, and more. [Diagrams documentation](https://diagrams.mingrammer.com/)

For your networking, Linux, AWS, and infrastructure work, this should be one of your primary packages.

## 2. Graphviz

Best for:

* Flowcharts
* Decision trees
* Legal process diagrams
* Organizational charts
* Dependency maps
* State transitions
* Evidence relationships
* Document workflows

```bash
pip install graphviz
```

```python
from graphviz import Digraph

diagram = Digraph("approval_process", format="svg")

diagram.attr(rankdir="TB")
diagram.node("A", "Document Submitted")
diagram.node("B", "Technical Review")
diagram.node("C", "Legal Review")
diagram.node("D", "Approved")
diagram.node("E", "Return for Revision")

diagram.edge("A", "B")
diagram.edge("B", "C")
diagram.edge("C", "D", label="Approved")
diagram.edge("C", "E", label="Changes required")
diagram.edge("E", "A")

diagram.render("approval-process", cleanup=True)
```

Graphviz is arguably the most important general-purpose diagram engine. It automatically calculates node placement and routing, making it excellent for diagrams generated by an AI agent.

Use it when the relationships matter more than custom artwork.

## 3. Schemdraw

Best for:

* Electrical circuits
* Wiring diagrams
* Logic gates
* Signal-flow diagrams
* Engineering illustrations
* Simple flowcharts

```bash
pip install schemdraw
```

```python
import schemdraw
import schemdraw.elements as elm

with schemdraw.Drawing() as drawing:
    drawing += elm.SourceV().label("12V")
    drawing += elm.Resistor().right().label("1kΩ")
    drawing += elm.LED().down().label("Status LED")
    drawing += elm.Line().left()
```

Schemdraw produces clean vector-style technical graphics and is especially valuable for hardware, structured cabling, electronics, and engineering documentation.

## 4. NetworkX

Best for:

* Network topology data
* Relationship analysis
* Dependency analysis
* Social networks
* Routing structures
* Finding paths, clusters, and central nodes

```bash
pip install networkx matplotlib
```

```python
import matplotlib.pyplot as plt
import networkx as nx

network = nx.Graph()

network.add_edges_from([
    ("Core Switch", "Access Switch 1"),
    ("Core Switch", "Access Switch 2"),
    ("Access Switch 1", "Server 1"),
    ("Access Switch 2", "Server 2"),
])

positions = nx.spring_layout(network, seed=42)

nx.draw(
    network,
    positions,
    with_labels=True,
    node_color="#10C8D8",
    node_size=3000,
    font_size=9
)

plt.show()
```

Important distinction: NetworkX is primarily a **graph-analysis package**, not a dedicated visualization system. Its documentation recommends using Graphviz, Matplotlib, or other visualization tools for more sophisticated presentation. [NetworkX documentation](https://networkx.org/documentation/stable/reference/drawing.html)

A strong combination is:

```text
NetworkX → calculates relationships
Graphviz → lays them out
SVG/PDF → final document output
```

## 5. PyVis

Best for:

* Interactive network maps
* Clickable relationship diagrams
* Browser-based topology exploration
* Large graphs that users need to move and inspect

```bash
pip install pyvis networkx
```

```python
from pyvis.network import Network

diagram = Network(height="700px", width="100%", directed=True)

diagram.add_node("router", label="Core Router")
diagram.add_node("switch", label="Access Switch")
diagram.add_node("server", label="Linux Server")

diagram.add_edge("router", "switch")
diagram.add_edge("switch", "server")

diagram.show("network.html")
```

PyVis creates interactive HTML where users can:

* Drag nodes
* Zoom
* Pan
* Hover for information
* Explore relationships

This is excellent for a live technical report but less suitable for a static legal PDF.

## 6. Erdantic

Best for automatically generating entity-relationship diagrams from:

* Pydantic models
* Dataclasses
* ORM models
* Structured Python classes

```bash
pip install erdantic
```

```python
from dataclasses import dataclass
import erdantic as erd

@dataclass
class Customer:
    name: str
    email: str

@dataclass
class Order:
    number: str
    customer: Customer

erd.draw(Order, out="order-model.svg")
```

This is extremely useful for:

* Database documentation
* API documentation
* Application design
* Data-governance documentation
* Legal or compliance data maps

## 7. Plotly

Best for:

* Interactive business charts
* Dashboards
* Timelines
* Sankey diagrams
* Geographic maps
* Statistical visualizations

```bash
pip install plotly pandas
```

Example Sankey diagram:

```python
import plotly.graph_objects as go

figure = go.Figure(
    go.Sankey(
        node={
            "label": [
                "Submitted",
                "Technical Review",
                "Legal Review",
                "Approved"
            ]
        },
        link={
            "source": [0, 1, 2],
            "target": [1, 2, 3],
            "value": [10, 8, 6]
        }
    )
)

figure.show()
```

Plotly is technically more of a data-visualization library than a traditional diagramming library, but its Sankey, timeline, treemap, and geographic capabilities are extremely valuable for professional reports.

## 8. Matplotlib

Best for:

* Custom static graphics
* Publication-quality charts
* Annotated illustrations
* Highly controlled layouts
* PDF and report integration

```bash
pip install matplotlib
```

Matplotlib requires more manual work than Graphviz, but it offers precise control over:

* Positioning
* Typography
* Lines and arrows
* Shapes
* Annotations
* Export resolution

Use it when other packages cannot create the exact visual required.

## 9. Blockdiag family

This is a collection of diagram-as-code tools:

* `blockdiag` — block diagrams
* `seqdiag` — sequence diagrams
* `actdiag` — activity diagrams
* `nwdiag` — network diagrams
* `rackdiag` — server-rack diagrams
* `packetdiag` — packet structures

These are especially interesting for a technical-documentation agent because each package addresses a specific diagram type.

```bash
pip install blockdiag seqdiag actdiag nwdiag
```

A network example:

```text
nwdiag {
    network internal {
        address = "10.0.0.0/24";

        router;
        switch;
        server;

        router -- switch;
        switch -- server;
    }
}
```

This syntax is not Python itself, but Python packages render it.

`rackdiag` is particularly relevant to your data-center and rack-documentation work.

## 10. PlantUML with Python

Best for formal software-engineering diagrams:

* Class diagrams
* Sequence diagrams
* Component diagrams
* Deployment diagrams
* Use-case diagrams
* State diagrams
* Activity diagrams

Python can generate PlantUML source and call a PlantUML renderer.

```bash
pip install plantuml
```

```python
diagram = """
@startuml
actor User
participant Application
database Database

User -> Application: Submit request
Application -> Database: Save request
Database --> Application: Confirmation
Application --> User: Success
@enduml
"""
```

PlantUML is an excellent choice when compliance with familiar UML conventions matters more than custom visual styling.

## My recommended toolkit

For the document-specialist agent you are designing, I would use:

```text
graphviz
diagrams
schemdraw
networkx
pyvis
erdantic
plotly
matplotlib
```

Add the Blockdiag family or PlantUML when those specific formal diagram types are needed.

### Best tool by assignment

| Assignment                       | Recommended package    |
| -------------------------------- | ---------------------- |
| AWS/cloud architecture           | `diagrams`             |
| Network topology                 | `diagrams` or `nwdiag` |
| Flowchart                        | `graphviz`             |
| Decision tree                    | `graphviz`             |
| Legal process                    | `graphviz`             |
| Organizational chart             | `graphviz`             |
| Electrical schematic             | `schemdraw`            |
| Rack elevation                   | `rackdiag`             |
| Packet structure                 | `packetdiag`           |
| Database model                   | `erdantic`             |
| Software UML                     | PlantUML               |
| Interactive network              | `pyvis`                |
| Network analysis                 | `networkx`             |
| Business chart                   | `plotly`               |
| Sankey diagram                   | `plotly`               |
| Highly customized static graphic | `matplotlib`           |

My strongest overall recommendation is:

> Use `Graphviz` as the general diagram engine, `Diagrams` for infrastructure architecture, `Schemdraw` for engineering visuals, and `Plotly` for data-driven visuals.

That combination would cover most technical, legal, business, and infrastructure documents your AI agent would encounter.


For **networking and cloud infrastructure diagrams**, I would build the agent around four layers:

1. `diagrams` for polished architecture drawings
2. `N2G` or custom Graphviz for network topology generation
3. `Netmiko`/`Nornir`/`NAPALM` for collecting live device information
4. Draw.io output for human editing

## Best packages by purpose

| Need                         | Best choice                                |
| ---------------------------- | ------------------------------------------ |
| Designed cloud architecture  | `diagrams`                                 |
| AWS-native diagram-as-code   | AWS `diagram-as-code`                      |
| Traditional network topology | `N2G` + Graphviz                           |
| Live device discovery        | `Nornir` + `Netmiko` or `NAPALM`           |
| CDP/LLDP discovery           | `Netmiko`, `NAPALM`, or `pyATS`            |
| Graph analysis               | `networkx`                                 |
| Editable Draw.io output      | `N2G` or direct Draw.io XML generation     |
| Interactive browser topology | `pyvis`                                    |
| Existing AWS account mapping | Cloud-provider APIs plus a custom renderer |
| Rack elevations              | `rackdiag` or custom SVG                   |
| Cable and port mapping       | Custom SVG/Draw.io generation              |

# 1. Diagrams: best for polished cloud architecture

This remains my top recommendation for designed cloud and infrastructure diagrams.

It supports:

* AWS
* Azure
* Google Cloud
* Kubernetes
* Oracle Cloud
* Alibaba Cloud
* OpenStack
* On-premises servers
* Cisco and generic network components
* SaaS products
* Common databases and frameworks

```bash
pip install diagrams
```

You must also install the Graphviz system package.

Ubuntu:

```bash
sudo apt install graphviz
```

Example hybrid network:

```python
from diagrams import Cluster, Diagram, Edge
from diagrams.aws.compute import EC2
from diagrams.aws.database import RDS
from diagrams.aws.network import (
    InternetGateway,
    NATGateway,
    PrivateSubnet,
    PublicSubnet,
    TransitGateway,
    VPC
)
from diagrams.onprem.client import Users
from diagrams.onprem.network import CiscoRouter
from diagrams.onprem.compute import Server

with Diagram(
    "Hybrid Infrastructure",
    filename="hybrid-infrastructure",
    show=False,
    direction="LR"
):
    users = Users("Remote Users")
    router = CiscoRouter("Edge Router")

    with Cluster("On-Premises"):
        local_servers = Server("Linux Servers")

    transit = TransitGateway("Transit Gateway")

    with Cluster("AWS"):
        with Cluster("Production VPC"):
            vpc = VPC("10.10.0.0/16")

            with Cluster("Public Subnet"):
                public = PublicSubnet("10.10.1.0/24")
                gateway = InternetGateway("Internet Gateway")
                nat = NATGateway("NAT Gateway")

            with Cluster("Private Subnet"):
                private = PrivateSubnet("10.10.10.0/24")
                application = EC2("Application")
                database = RDS("Database")

    users >> router
    router >> Edge(label="VPN") >> transit
    transit >> vpc
    gateway >> public >> nat
    nat >> private >> application >> database
    router >> local_servers
```

`diagrams` is designed for architecture rather than live discovery. It does not inspect or configure your actual cloud resources. [Diagrams documentation](https://diagrams.mingrammer.com/)

# 2. N2G: best Python library for editable network diagrams

The relevant N2G here means **Need to Graph**, a Python diagram-generation library. It can generate:

* Draw.io diagrams
* yEd diagrams
* Interactive 3D visualizations
* Network topologies
* Layer 2 maps
* Layer 3 maps

Install:

```bash
pip install N2G
```

A simple Draw.io topology:

```python
from N2G import drawio_diagram

diagram = drawio_diagram()
diagram.add_diagram("Network Topology")

diagram.add_node(
    id="core-01",
    label="Core Switch\n10.0.0.1"
)

diagram.add_node(
    id="access-01",
    label="Access Switch 1\n10.0.0.11"
)

diagram.add_node(
    id="access-02",
    label="Access Switch 2\n10.0.0.12"
)

diagram.add_link(
    source="core-01",
    target="access-01",
    label="TenGig1/1 ↔ TenGig1/1"
)

diagram.add_link(
    source="core-01",
    target="access-02",
    label="TenGig1/2 ↔ TenGig1/1"
)

diagram.dump_file(
    filename="network-topology.drawio",
    folder="./output"
)
```

The major advantage is the `.drawio` output. An engineer can open the generated topology and manually reposition, annotate, or correct it.

That makes it better than a PNG-only system for professional network documentation.

# 3. Nornir: orchestration and inventory

Nornir does not make diagrams itself. It manages network-device inventory and runs collection tasks concurrently.

```bash
pip install nornir nornir-netmiko
```

Use it to gather:

* CDP neighbors
* LLDP neighbors
* Interface information
* VLAN assignments
* Port channels
* Routing neighbors
* IP addresses
* Device models
* Serial numbers
* Software versions

The data can then be passed to N2G, Graphviz, or Draw.io generation.

The workflow would be:

```mermaid
flowchart LR
    A["Nornir inventory"] --> B["Netmiko or NAPALM"]
    B --> C["CDP, LLDP and interfaces"]
    C --> D["Normalize topology data"]
    D --> E["N2G or Graphviz"]
    E --> F["Draw.io, SVG and PDF"]
```

# 4. Netmiko: command collection

Netmiko is useful when the topology agent needs to SSH into Cisco or other network devices and execute commands.

```bash
pip install netmiko
```

Example:

```python
from netmiko import ConnectHandler

device = {
    "device_type": "cisco_ios",
    "host": "10.0.0.1",
    "username": "admin",
    "password": "password"
}

with ConnectHandler(**device) as connection:
    cdp = connection.send_command(
        "show cdp neighbors detail",
        use_textfsm=True
    )

    interfaces = connection.send_command(
        "show interfaces status",
        use_textfsm=True
    )
```

The agent could combine the neighbor data from multiple switches and construct the topology automatically.

Credentials should come from environment variables or a secret manager—not source code.

# 5. NAPALM: normalized multi-vendor data

NAPALM gives a relatively consistent Python interface across supported vendors.

```bash
pip install napalm
```

It can retrieve information such as:

* Interfaces
* LLDP neighbors
* ARP entries
* MAC information
* BGP neighbors
* Device facts
* Configuration
* VLANs

```python
from napalm import get_network_driver

driver = get_network_driver("ios")
device = driver(
    hostname="10.0.0.1",
    username="admin",
    password="password"
)

device.open()

facts = device.get_facts()
interfaces = device.get_interfaces()
neighbors = device.get_lldp_neighbors_detail()

device.close()
```

NAPALM is especially valuable when the environment contains multiple vendors because it reduces the amount of vendor-specific parsing.

# 6. Cisco pyATS and Genie

For Cisco-heavy environments, `pyATS` and Genie are extremely valuable.

They can:

* Connect to Cisco devices
* Parse command output into structured data
* Learn network features
* Compare network states
* Validate configurations
* Identify changes between snapshots

```bash
pip install pyats genie
```

Instead of manually parsing this:

```text
Device ID    Local Intrfce    Holdtme    Capability
SBN-SW-02    Ten 1/1          122        R S I
```

Genie can convert command output into structured dictionaries that your agent can feed into a diagram generator.

For your Cisco background, a strong stack would be:

```text
pyATS/Genie → structured Cisco data
NetworkX    → topology model and analysis
N2G         → editable Draw.io diagram
Graphviz    → polished SVG/PDF
```

# 7. NetworkX: topology intelligence

NetworkX should represent the topology internally, even if another package draws it.

```python
import networkx as nx

topology = nx.MultiGraph()

topology.add_node(
    "core-01",
    role="core",
    management_ip="10.0.0.1"
)

topology.add_node(
    "access-01",
    role="access",
    management_ip="10.0.0.11"
)

topology.add_edge(
    "core-01",
    "access-01",
    local_interface="TenGig1/1",
    remote_interface="TenGig1/1",
    link_type="trunk"
)
```

NetworkX lets the agent answer questions such as:

* Is a device disconnected?
* Are there redundant paths?
* Which switch is a single point of failure?
* What devices depend on this core switch?
* Are there unexpected topology loops?
* What changed since the previous discovery?
* What is the shortest path between two devices?

NetworkX itself provides basic drawing, but its documentation states that analysis—not advanced visualization—is its primary purpose. [NetworkX documentation](https://networkx.org/documentation/stable/reference/drawing.html)

# 8. AWS diagram-as-code

AWS Labs has a diagram-as-code project that describes AWS architecture in YAML.

It is useful when you want:

* AWS-specific architecture
* Human-readable YAML
* Git version control
* Repeatable rendering
* Standardized AWS visuals

[The AWS Labs project](https://github.com/awslabs/diagram-as-code) focuses specifically on generating AWS infrastructure diagrams from YAML.

This could be easier for an AI agent than writing complex layout logic directly in Python.

# 9. Existing cloud-environment discovery

There is an important difference between:

### Designed architecture

You tell the agent what the cloud should look like.

Use:

* `diagrams`
* AWS diagram-as-code
* Graphviz

### Discovered architecture

The agent connects to a cloud account and determines what currently exists.

Use:

* `boto3` for AWS
* Azure SDK for Python
* Google Cloud Python SDKs
* NetworkX for the internal resource graph
* N2G, Graphviz, or custom SVG for rendering

For AWS:

```bash
pip install boto3 networkx
```

```python
import boto3

ec2 = boto3.client("ec2")

vpcs = ec2.describe_vpcs()["Vpcs"]
subnets = ec2.describe_subnets()["Subnets"]
route_tables = ec2.describe_route_tables()["RouteTables"]
instances = ec2.describe_instances()["Reservations"]
security_groups = ec2.describe_security_groups()["SecurityGroups"]
```

The agent can build relationships among:

* Organizations and accounts
* Regions
* VPCs
* Availability Zones
* Public and private subnets
* Route tables
* Internet gateways
* NAT gateways
* Transit gateways
* VPN connections
* EC2 instances
* Load balancers
* Databases
* Security groups
* VPC endpoints

The original open-source CloudMapper once offered AWS network visualization, but its repository now says that visualization functionality is no longer maintained. I would not build a new agent around that original implementation. [CloudMapper repository](https://github.com/duo-labs/cloudmapper)

# My strongest recommendation

For a serious network and cloud document agent, use:

```text
Collection
├── Nornir
├── Netmiko
├── NAPALM
├── pyATS/Genie
├── boto3
├── Azure SDK
└── Google Cloud SDK

Modeling and analysis
├── NetworkX
├── Pydantic
└── Pandas

Diagram generation
├── N2G
├── Diagrams
├── Graphviz
└── PyVis

Output
├── Draw.io
├── SVG
├── PDF
├── PNG
└── Interactive HTML
```

## Best practical combination for you

Given your Cisco, AWS, Linux, cabling, and data-center interests, I would choose:

1. **`pyATS/Genie`** for Cisco discovery and parsing.
2. **`Nornir`** for managing multiple devices.
3. **`NetworkX`** for building and analyzing the topology.
4. **`N2G`** for editable Draw.io network maps.
5. **`diagrams`** for polished cloud and hybrid architecture.
6. **`Graphviz`** for clean automated layouts and SVG/PDF export.
7. **`boto3`** for discovering actual AWS infrastructure.

That stack could support both:

* **As-designed documentation:** what the infrastructure is supposed to be.
* **As-built documentation:** what discovery shows actually exists.

A high-quality agent should preserve both and flag differences between them.
