An over-engineered and opinionated tool that retrieves essential network information from devices using gNMI and OpenConfig models. Designed primarily for LLMs with Model Context Protocol (MCP) integration, it also provides a full CLI for direct use.
Opinionated by design, over-engineered by passion. gNMI and YANG expose overwhelming amounts of data with countless parameters. This tool provides what I consider the most relevant information for LLMs. And who doesn't enjoy building complicated solutions.
Retrieve structured network data in JSON format:
- π Routing: BGP, ISIS protocols and neighbor states
- π Interfaces: Status, configuration, and statistics
- π·οΈ MPLS: Labels, forwarding tables, and segment routing
- π VPN/VRF: L3VPN configuration and route targets
- π Logs: Filtered device logs with keyword search
- π Topology: Device neighbors and network-wide topology analysis
See the API definition for all available APIs and options.
- Python
3.13+
uv
, see the docs to install it.brew
is recommended for macOS users
- Network devices with gNMI enabled.
Windows users: The repo require a Unix-like environment. Use WSL.
Devices must support gNMI and OpenConfig models listed below:
OpenConfig Models dependencies
openconfig-system >= 0.17.1
openconfig-interfaces >= 3.0.0
openconfig-network-instance >= 1.3.0
Note
If the required model for a function is not found, gNMIBuddy will return an error. If the model version is older than required, it will continue execution but warn the user about potential errors.
You can use uv run gnmibuddy.py device capabilities
to verify the supported models on a specific device. See the options available.
Tested on:
- Cisco XRd Control Plane (
24.4.1.26I
)
Note
The get_logs()
function only works on IOS-XR.
gNMIBuddy identifies devices by hostname and looks up their corresponding IP addresses and credentials from the inventory file.
Caution
Without a device inventory file, gNMIBuddy cannot operate.
Provide device inventory via --inventory PATH
or set NETWORK_INVENTORY
env var.
Tip
Store environment variables in a .env
file.
The inventory must be a JSON list of Device
objects with these required fields:
name
: Device hostnameip_address
: IP for gNMI connectionsnos
: Network OS identifieriosxr
only for now, use it even if you have other NOS. More will be added later.
Authentication (choose one method):
- Username/Password: Requires both
username
andpassword
fields - Certificate-based: Requires both
path_cert
andpath_key
fields
Schema: src/schemas/models.py
| Example: xrd_sandbox.json
[
{
"name": "xrd-1",
"ip_address": "10.10.20.101",
"nos": "iosxr",
"username": "cisco",
"password": "C1sco12345"
},
{
"name": "xrd-2",
"ip_address": "10.10.20.102",
"nos": "iosxr",
"path_cert": "/opt/certs/device.pem",
"path_key": "/opt/certs/device.key"
}
]
Tip
Validate your inventory: Use gnmibuddy inventory validate
to check your inventory file for proper format, valid IP addresses, required fields, and authentication configuration before running network commands.
Fastest way to try gNMIBuddy:
# Replace `xrd_sandbox.json` with your actual inventory file
echo '#!/usr/bin/env bash' > /tmp/gnmibuddy-mcp-wrapper \
&& echo 'exec uvx --from git+https://github.com/jillesca/gNMIBuddy.git gnmibuddy-mcp "$@"' >> /tmp/gnmibuddy-mcp-wrapper \
&& chmod +x /tmp/gnmibuddy-mcp-wrapper \
&& NETWORK_INVENTORY=xrd_sandbox.json npx @modelcontextprotocol/inspector /tmp/gnmibuddy-mcp-wrapper
Tip
No repo cloning, no MCP client setup required! If you don't have XRd, see Testing with DevNet Sandbox.
π Have an MCP Client? (VSCode, Cursor, Claude Desktop)
Recommended: No installation required - runs directly from GitHub using uvx
:
MCP Client | Configuration |
---|---|
VSCode | π Copy config |
Standard MCP Clients | π Copy config |
For Development - when you need to test local changes:
MCP Client | Configuration |
---|---|
VSCode | π Copy config |
Standard MCP Clients | π Copy config |
The "Standard MCP Clients" config works with any MCP client following the MCP specification (Cursor, Claude Desktop, etc.). VSCode uses a different format.
Setup:
- uvx configs: Update the
NETWORK_INVENTORY
path to your inventory file - dev configs: Update the
NETWORK_INVENTORY
path andcwd
to your local project directory
π οΈ CLI Usage (Direct Tool Usage)
For CLI users who want to use gNMIBuddy as a command-line tool:
# Run directly without installation
uvx --from git+https://github.com/jillesca/gNMIBuddy.git gnmibuddy --help
# Example with commands
uvx --from git+https://github.com/jillesca/gNMIBuddy.git gnmibuddy --inventory your_inventory.json device list
# Install the tool globally
uv tool install git+https://github.com/jillesca/gNMIBuddy.git
# Use it directly
gnmibuddy --help
gnmibuddy device info --device R1
# Uninstall when no longer needed
uv tool uninstall gnmibuddy
# To get updates
uv tool upgrade gnmibuddy
The uvx
method automatically builds and runs the tool in an isolated environment without affecting your system.
# Clone and setup (one-time only)
git clone https://github.com/jillesca/gNMIBuddy.git && cd gNMIBuddy
# Install dependencies
uv sync --frozen --no-dev
β― uv run gnmibuddy.py --help
ββββββ ββββ βββββββββββ ββ ββββββ ββββββ ββ
ββ ββββββββββββ β ββ ββββ ββββ βββ βββββ
βββββββ βββββ ββ β βββββββ ββββ βββ β ββ
βββββββ ββββ βββββββββββββββββββββββββββ ββ
An opinionated tool that retrieves essential network information from devices using gNMI and OpenConfig models.
Designed primarily for LLMs with Model Context Protocol (MCP) integration, it also provides a full CLI.
Help: https://github.com/jillesca/gNMIBuddy
Python Version: 3.13.4
gNMIBuddy Version: 0.1.0
Usage:
gnmibuddy.py [OPTIONS] COMMAND [ARGS]...
π Inventory Requirement:
Provide device inventory via --inventory PATH, set NETWORK_INVENTORY env var, or use .env file (configurable with --env-file PATH)
Options:
-h, --help Show this message and exit
-V, --version Show version information
--log-level LEVEL Set logging level (debug, info, warning, error)
--module-log-help Show detailed module logging help
--all-devices Run on all devices concurrently
--inventory PATH Path to inventory JSON file
-e, --env-file PATH Path to .env file for configuration (default: .env in project root)
--max-workers NUMBER Maximum number of concurrent workers for batch operations (--all-devices, --devices, --device-file)
Commands:
device (d) Device Information
capabilities Get gNMI capabilities from a network device
info Get system information from a network device
list List all available devices in the inventory
profile Get device profile and role information
network (n) Network Protocols
interface Get interface status and configuration
mpls Get MPLS forwarding and label information
routing Get routing protocol information (BGP, ISIS, OSPF)
vpn Get VPN/VRF configuration and status
topology (t) Network Topology
neighbors Get direct neighbor information via LLDP/CDP
adjacency Get network-wide IP adjacency analysis for complete topology
network Get complete network topology information. Queries all devices in inventory.
ops (o) Operations
logs Retrieve and filter device logs
validate Validate all collector functions (development tool)
inventory (i) Inventory Management
validate Validate inventory file format and schema
Examples:
gnmibuddy.py device info --device R1
gnmibuddy.py network routing --device R1
gnmibuddy.py --all-devices device list
gnmibuddy.py inventory validate --inventory inventory.json
gnmibuddy.py --env-file production.env device list
gnmibuddy.py --env-file dev.env --log-level debug device info --device R1
Run 'gnmibuddy.py COMMAND --help' for more information on a command.
Recommended: Use uvx (no repository clone needed):
# Replace `xrd_sandbox.json` with your actual inventory file
echo '#!/usr/bin/env bash' > /tmp/gnmibuddy-mcp-wrapper \
&& echo 'exec uvx --from git+https://github.com/jillesca/gNMIBuddy.git gnmibuddy-mcp "$@"' >> /tmp/gnmibuddy-mcp-wrapper \
&& chmod +x /tmp/gnmibuddy-mcp-wrapper \
&& NETWORK_INVENTORY=xrd_sandbox.json npx @modelcontextprotocol/inspector /tmp/gnmibuddy-mcp-wrapper
EOF
For local development (testing uncommitted changes):
# Run from your gNMIBuddy project directory (where pyproject.toml is located)
cd /path/to/your/gNMIBuddy && \
NETWORK_INVENTORY=your_inventory.json \
npx @modelcontextprotocol/inspector \
uv run --frozen gnmibuddy-mcp
Choose the approach that fits your needs:
Use Case | VSCode | Standard MCP Clients |
---|---|---|
Production/Testing | π Copy config | π Copy config |
Local Development | π Copy config | π Copy config |
Standard MCP Clients config works with Cursor, Claude Desktop, and any other client following the MCP specification. VSCode requires a specific format.
Configuration requirements:
- uvx configs: Only update
NETWORK_INVENTORY
path to your inventory file - dev configs: Update both
NETWORK_INVENTORY
path andcwd
to your local project directory
Don't have network devices? Use the DevNet XRd Sandbox, follow the instructions to bring up a MPLS network with docker, then configure gNMI with the included Ansible playbook:
# If you cloned the repo
# Enable gRPC on the DevNet XRd Sandbox
ANSIBLE_HOST_KEY_CHECKING=False \
uvx --from ansible-core --with "paramiko,ansible" \
ansible-playbook ansible-helper/xrd_apply_config.yaml -i ansible-helper/hosts
If you didn't clone the repo use this command
# Self-contained command that downloads files automatically
ANSIBLE_HOST_KEY_CHECKING=False \
bash -c 'TMPDIR=$(mktemp -d) \
&& trap "rm -rf $TMPDIR" EXIT \
&& curl -s https://raw.githubusercontent.com/jillesca/gNMIBuddy/refs/heads/main/ansible-helper/xrd_apply_config.yaml > "$TMPDIR/playbook.yaml" \
&& curl -s https://raw.githubusercontent.com/jillesca/gNMIBuddy/refs/heads/main/ansible-helper/hosts > "$TMPDIR/hosts" \
&& uvx --from ansible-core --with "paramiko,ansible" ansible-playbook "$TMPDIR/playbook.yaml" -i "$TMPDIR/hosts"'
Test with the xrd_sandbox.json
inventory file part of the repository.
If you have problems with Ansible
Enable manually gNMI. Apply this configuration to all XRd devices:
grpc
port 57777
no-tls
Don't forget to commit
your changes to XRd.
Want to see how this MCP tool integrates with actual AI agents? Check out sp_oncall - a graph of agents that use gNMIBuddy to demonstrate real-world network operations scenarios.
gNMIBuddy provides structured, consistent responses for all network operations. The response format depends on whether you're targeting a single device or multiple devices.
Single device operations return a NetworkOperationResult
object with detailed information about the operation, including status, data, metadata, and error handling.
@dataclass
class NetworkOperationResult:
device_name: str
ip_address: str
nos: str
operation_type: str
status: OperationStatus
data: Dict[str, Any] = field(default_factory=dict)
metadata: Dict[str, Any] = field(default_factory=dict)
error_response: Optional[ErrorResponse] = None
feature_not_found_response: Optional[FeatureNotFoundResponse] = None
Batch operations (using --all-devices
, --devices
, or --device-file
) return a BatchOperationResult
object containing:
results
: A list ofNetworkOperationResult
objects, one for each devicesummary
: Aggregate statistics about the batch operationmetadata
: Additional batch operation metadata
@dataclass
class BatchOperationResult:
results: List[NetworkOperationResult] # One result per device
summary: BatchOperationSummary
metadata: Dict[str, Any] = field(default_factory=dict)
For more details, see the response schema definition.
gNMIBuddy uses a centralized schema approach for data contracts:
src/schemas/
: Contains all shared data models and response contracts.src/collectors/
: Network telemetry data collectors following OpenTelemetry patterns.src/processors/
: Data transformation processors following OpenTelemetry patterns.
These schemas serve as contracts between different parts of the system, ensuring consistency across:
- CLI and API interfaces.
- Network operation responses.
- Error handling and status reporting.
- MCP tool integration.
The application follows an OpenTelemetry-inspired architecture:
Raw gNMI Data β Collector β Processor β Schema β Response
- Collectors gather data from network devices via gNMI.
- Processors transform raw data into structured, LLM-friendly formats.
- Schemas ensure consistent data contracts across the system.
- Responses provide standardized output for CLI, API, and MCP interfaces.
gNMIBuddy supports environment variables for configuration, which work for both CLI and MCP server usage. Environment variables can be loaded from:
- Command line arguments (highest priority)
- Operating system environment variables
.env
files (default:.env
in project root)- Default values (lowest priority)
gNMIBuddy automatically loads environment variables from a .env
file in the project root. You can specify a custom .env
file using the --env-file
option:
# Use default .env file
gnmibuddy device list
# Use custom environment file
gnmibuddy --env-file production.env device list
Example:
# .env file
# Network configuration
NETWORK_INVENTORY=/path/to/inventory.json
# Logging configuration
GNMIBUDDY_LOG_LEVEL=debug
GNMIBUDDY_MODULE_LEVELS=src.cmd=warning,src.inventory=debug
GNMIBUDDY_STRUCTURED_LOGGING=true
GNMIBUDDY_LOG_FILE=/custom/log/path.log
GNMIBUDDY_EXTERNAL_SUPPRESSION_MODE=development
# MCP debugging
GNMIBUDDY_MCP_TOOL_DEBUG=true
Variable | Description | Values | Default |
---|---|---|---|
NETWORK_INVENTORY |
Device inventory file path | File path | - |
GNMIBUDDY_LOG_LEVEL |
Global log level | debug , info , warning , error |
info |
GNMIBUDDY_MODULE_LEVELS |
Module-specific log levels | module1=debug,module2=warning |
- |
GNMIBUDDY_LOG_FILE |
Custom log file path (overrides sequential) | File path | logs/gnmibuddy_XXX.log |
GNMIBUDDY_STRUCTURED_LOGGING |
Enable JSON logging | true , false |
false |
GNMIBUDDY_EXTERNAL_SUPPRESSION_MODE |
External library suppression | cli , mcp , development |
cli |
GNMIBUDDY_MCP_TOOL_DEBUG |
Enable MCP tool debugging | true , false |
false |
Sequential Log Files: gNMIBuddy automatically creates numbered log files (gnmibuddy_001.log
, gnmibuddy_002.log
, etc.) for each execution in the logs/
directory. The highest number is always the most recent run.
Note
Environment variables serve as defaults and can be overridden by CLI arguments like --log-level
and --module-log-levels
.
For detailed environment configuration options and advanced usage, see Environment Configuration Guide
For complete logging environment variable documentation, see Logging README
gNMIBuddy supports running commands across multiple devices simultaneously with configurable concurrency controls to optimize performance while avoiding rate limiting.
Device Selection:
--device DEVICE
: Single device operation--devices device1,device2,device3
: Comma-separated device list--device-file path/to/devices.txt
: Device list from file (one per line)--all-devices
: Run on all devices in inventory
Concurrency Controls:
--max-workers N
: Maximum concurrent devices to process (default: 5)--per-device-workers N
: Maximum concurrent operations per device (default: varies by command)
gNMIBuddy operates with two levels of concurrency:
- Device-level concurrency (
--max-workers
): How many devices to process simultaneously - Per-device concurrency (command-specific): How many operations to run simultaneously on each device
Total concurrent requests = max_workers Γ per_device_operations
# Process 3 devices, 2 operations per device = 6 total requests
uv run gnmibuddy.py --max-workers 3 ops validate --devices xrd-1,xrd-2,xrd-3 --per-device-workers 2