scriptoriumadmin/litellm
1
0
Fork 0
mirror of https://github.com/BerriAI/litellm.git synced 2026-10-04 02:31:27 +00:00
Code Issues Projects Releases Packages Wiki Activity Actions 69

feat(anthropic): surface compaction usage iterations data

Browse source
This commit is contained in:
Dushyant Acharya 2026-06-14 05:56:05 +05:30
parent 2655d1dd5e
commit 548ae6a089
No known key found for this signature in database
3 changed files with 125 additions and 13 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

42
litellm/llms/anthropic/chat/transformation.py
View file

@ -2214,18 +2214,33 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig):
if "inference_geo" in _usage and _usage["inference_geo"] is not None:
inference_geo = _usage["inference_geo"]
if (
"cache_creation_input_tokens" in _usage
and _usage["cache_creation_input_tokens"] is not None
):
cache_creation_input_tokens = _usage["cache_creation_input_tokens"]
prompt_tokens += cache_creation_input_tokens
if (
"cache_read_input_tokens" in _usage
and _usage["cache_read_input_tokens"] is not None
):
cache_read_input_tokens = _usage["cache_read_input_tokens"]
prompt_tokens += cache_read_input_tokens
iterations: Optional[List[Any]] = _usage.get("iterations")
if iterations:
prompt_tokens = sum(it.get("input_tokens", 0) or 0 for it in iterations)
completion_tokens = sum(
it.get("output_tokens", 0) or 0 for it in iterations
)
cache_creation_input_tokens = sum(
it.get("cache_creation_input_tokens", 0) or 0 for it in iterations
)
cache_read_input_tokens = sum(
it.get("cache_read_input_tokens", 0) or 0 for it in iterations
)
prompt_tokens += cache_creation_input_tokens + cache_read_input_tokens
if not iterations:
if (
"cache_creation_input_tokens" in _usage
and _usage["cache_creation_input_tokens"] is not None
):
cache_creation_input_tokens = _usage["cache_creation_input_tokens"]
prompt_tokens += cache_creation_input_tokens
if (
"cache_read_input_tokens" in _usage
and _usage["cache_read_input_tokens"] is not None
):
cache_read_input_tokens = _usage["cache_read_input_tokens"]
prompt_tokens += cache_read_input_tokens
if "server_tool_use" in _usage and _usage["server_tool_use"] is not None:
if (
"web_search_requests" in _usage["server_tool_use"]
@ -2264,7 +2279,7 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig):
),
)
raw_input_tokens = usage_object.get("input_tokens", 0) or 0
raw_input_tokens = prompt_tokens - cache_read_input_tokens - cache_creation_input_tokens
prompt_tokens_details = PromptTokensDetailsWrapper(
cached_tokens=cache_read_input_tokens,
cache_creation_tokens=cache_creation_input_tokens,
@ -2296,6 +2311,7 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig):
cache_creation_input_tokens=cache_creation_input_tokens,
cache_read_input_tokens=cache_read_input_tokens,
completion_tokens_details=completion_token_details,
iterations=iterations,
server_tool_use=(
ServerToolUse(
web_search_requests=web_search_requests,

95
tests/test_anthropic_compaction_usage.py Normal file
View file

@ -0,0 +1,95 @@
from litellm.llms.anthropic.chat.transformation import AnthropicConfig
def test_anthropic_compaction_usage_calculation():
"""
Test that calculate_usage correctly sums tokens from the iterations array
as requested in Issue #27060.
"""
anthropic_config = AnthropicConfig()
# Mock usage object with compaction iterations
usage_object = {
"input_tokens": 100, # Top-level (excludes compaction)
"output_tokens": 50, # Top-level (excludes compaction)
"iterations": [
{
"iteration": 1,
"type": "compaction",
"input_tokens": 1000,
"output_tokens": 500
},
{
"iteration": 2,
"type": "message",
"input_tokens": 100,
"output_tokens": 50
}
]
}
usage = anthropic_config.calculate_usage(
usage_object=usage_object,
reasoning_content=None
)
# Assertions
# Total prompt tokens should be 1000 + 100 = 1100
assert usage.prompt_tokens == 1100
# Total completion tokens should be 500 + 50 = 550
assert usage.completion_tokens == 550
# Total tokens should be 1650
assert usage.total_tokens == 1650
# Assert details
assert usage.prompt_tokens_details.text_tokens == 1100
# Assert iterations passthrough
assert usage.iterations is not None
assert len(usage.iterations) == 2
assert usage.iterations[0]["type"] == "compaction"
def test_anthropic_compaction_usage_with_iteration_cache():
"""
Test that calculate_usage correctly sums caching tokens FROM iterations.
This covers the specific case mentioned by JasonPan.
"""
anthropic_config = AnthropicConfig()
usage_object = {
"input_tokens": 100,
"output_tokens": 50,
"iterations": [
{
"type": "compaction",
"input_tokens": 500,
"output_tokens": 200,
"cache_creation_input_tokens": 50,
"cache_read_input_tokens": 17000
},
{
"type": "message",
"input_tokens": 100,
"output_tokens": 50,
"cache_creation_input_tokens": 10,
"cache_read_input_tokens": 20
}
]
}
usage = anthropic_config.calculate_usage(
usage_object=usage_object,
reasoning_content=None
)
# input_tokens sum = 500 + 100 = 600
# cache_creation sum = 50 + 10 = 60
# cache_read sum = 17000 + 20 = 17020
# Total prompt tokens = 600 + 60 + 17020 = 17680
assert usage.prompt_tokens == 17680
assert usage.completion_tokens == 250
assert usage.prompt_tokens_details.cache_creation_tokens == 60
assert usage.prompt_tokens_details.cached_tokens == 17020
if __name__ == "__main__":
test_anthropic_compaction_usage_calculation()
test_anthropic_compaction_usage_with_iteration_cache()

1
tests/test_litellm/proxy/spend_tracking/test_spend_management_endpoints.py
View file

@ -359,6 +359,7 @@ ignored_keys = [
"metadata.additional_usage_values.cache_read_input_tokens",
"metadata.additional_usage_values.inference_geo",
"metadata.additional_usage_values.speed",
"metadata.additional_usage_values.iterations",
"metadata.litellm_overhead_time_ms",
"metadata.cost_breakdown",
"metadata.user_api_key",