Depending on your input data and if its structure is known and consistent, it will be more hard or easy.
In my example here the idea is to read the file line by line with a lazy generator and write new files whenever a valid object can be constructed from the input. Its a bit like manual parsing.
In the real world case this logic when to write to a new file would highly depend on your input and what you are trying to achieve.
Some sample data
[
{
"color": "red",
"value": "#f00"
},
{
"color": "green",
"value": "#0f0"
},
{
"color": "blue",
"value": "#00f"
},
{
"color": "cyan",
"value": "#0ff"
},
{
"color": "magenta",
"value": "#f0f"
},
{
"color": "yellow",
"value": "#ff0"
},
{
"color": "black",
"value": "#000"
}
]
# create a generator that yields each individual line
lines = (l for l in open('data.json'))
# o is used to accumulate some lines before
# writing to the files
o=''
# itemCount is used to count the number of valid json objects
itemCount=0
# read the file line by line to avoid memory issues
i=-1
while True:
try:
line = next(lines)
except StopIteration:
break
i=i+1
# ignore first square brackets
if i == 0:
continue
# in this data I know every 5th lines a new object will begin
# this logic depends on your input data
if i%4==0:
itemCount+=1
# at this point I am able to create avalid json object
# based on my knowledge of the input file structure
validObject=o+line.replace("},\n", '}\n')
o=''
# now write each object to its own file
with open(f'item-{itemCount}.json', 'w') as outfile:
outfile.write(validObject)
else:
o+=line

Here is a repl with the working example: https://replit.com/@bluebrown/linebyline