Native Shell
Native Shell은 모든 플랫폼의 앱 안에서 만들어지는 AI UI입니다
메이커가 쓴 소개는 위의 한 줄이 전부입니다. 더 자세한 내용은 저장소 README에서 읽을 수 있습니다. 저장소 README ↗
README 전체를 불러오는 중입니다.
README 전문 보기 ↗Muninn is a library that transforms unstructured network device CLI output into structured, type-hinted Python data structures.
Muninn().parse(os, command, output) and you're done.TypedDict return types that describe the parsed data structure, enabling IDE autocompletion and better AI-assisted coding.Muninn can be quickly and easily installed with uv as shown below:
uv add muninn-parsers
Or, if you prefer good old-fashioned pip, you can do so as shown below:
pip install muninn-parsers
Create a Muninn instance and call parse() with an OS identifier, the CLI command, and the raw output. Muninn automatically finds and runs the right parser:
from typing import Any
import muninn
mn = muninn.Muninn()
raw_output = """
Neighbor ID Pri State Dead Time Address Interface
10.1.1.1 1 FULL/DR 00:00:38 192.168.1.1 Ethernet1/1
10.1.1.2 1 FULL/BDR 00:00:33 192.168.1.2 Ethernet1/2
"""
result: dict[str, Any] = mn.parse("nxos", "show ip ospf neighbor", raw_output)
Returns:
{
"10.1.1.1": {
"priority": 1,
"state": "FULL/DR",
"dead_time": "00:00:38",
"address": "192.168.1.1",
"interface": "Ethernet1/1"
},
"10.1.1.2": {
"priority": 1,
"state": "FULL/BDR",
"dead_time": "00:00:33",
"address": "192.168.1.2",
"interface": "Ethernet1/2"
}
}
You can also import a specific parser and call it directly. This gives you access to the parser's TypedDict return type, which enables IDE autocompletion and helps AI coding assistants reason about the structure of the parsed data:
from muninn.parsers.nxos.show_ip_ospf_neighbor import (
ShowIpOspfNeighborParser,
ShowIpOspfNeighborResult,
)
raw_output = """
Neighbor ID Pri State Dead Time Address Interface
10.1.1.1 1 FULL/DR 00:00:38 192.168.1.1 Ethernet1/1
10.1.1.2 1 FULL/BDR 00:00:33 192.168.1.2 Ethernet1/2
"""
result: ShowIpOspfNeighborResult = ShowIpOspfNeighborParser.parse(raw_output)
# IDE autocompletion and type checking work here
Full documentation is available at chartinolabs.github.io/Muninn.
Early development. See the Design Philosophy page for project direction.