mirror of
https://github.com/NousResearch/hermes-agent.git
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b9463e32c6
Port from cline/cline#10266. When OpenAI-compatible proxies (OpenRouter, Vercel AI Gateway, Cline) route Claude models, they sometimes surface the Anthropic-native cache counters (`cache_read_input_tokens`, `cache_creation_input_tokens`) at the top level of the `usage` object instead of nesting them inside `prompt_tokens_details`. Our chat-completions branch of `normalize_usage()` only read the nested `prompt_tokens_details` fields, so those responses: - reported `cache_write_tokens = 0` even when the model actually did a prompt-cache write, - reported only some of the cache-read tokens when the proxy exposed them top-level only, - overstated `input_tokens` by the missed cache-write amount, which in turn made cost estimation and the status-bar cache-hit percentage wrong for Claude traffic going through these gateways. Now the chat-completions branch tries the OpenAI-standard `prompt_tokens_details` first and falls back to the top-level Anthropic-shape fields only if the nested values are absent/zero. The Anthropic and Codex Responses branches are unchanged. Regression guards added for three shapes: top-level write + nested read, top-level-only, and both-present (nested wins).
192 lines
6.5 KiB
Python
192 lines
6.5 KiB
Python
from types import SimpleNamespace
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from agent.usage_pricing import (
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CanonicalUsage,
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estimate_usage_cost,
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get_pricing_entry,
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normalize_usage,
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)
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def test_normalize_usage_anthropic_keeps_cache_buckets_separate():
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usage = SimpleNamespace(
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input_tokens=1000,
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output_tokens=500,
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cache_read_input_tokens=2000,
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cache_creation_input_tokens=400,
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)
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normalized = normalize_usage(usage, provider="anthropic", api_mode="anthropic_messages")
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assert normalized.input_tokens == 1000
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assert normalized.output_tokens == 500
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assert normalized.cache_read_tokens == 2000
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assert normalized.cache_write_tokens == 400
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assert normalized.prompt_tokens == 3400
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def test_normalize_usage_openai_subtracts_cached_prompt_tokens():
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usage = SimpleNamespace(
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prompt_tokens=3000,
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completion_tokens=700,
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prompt_tokens_details=SimpleNamespace(cached_tokens=1800),
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)
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normalized = normalize_usage(usage, provider="openai", api_mode="chat_completions")
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assert normalized.input_tokens == 1200
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assert normalized.cache_read_tokens == 1800
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assert normalized.output_tokens == 700
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def test_normalize_usage_openai_reads_top_level_anthropic_cache_fields():
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"""Some OpenAI-compatible proxies (OpenRouter, Vercel AI Gateway, Cline) expose
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Anthropic-style cache token counts at the top level of the usage object when
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routing Claude models, instead of nesting them in prompt_tokens_details.
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Regression guard for the bug fixed in cline/cline#10266 — before this fix,
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the chat-completions branch of normalize_usage() only read
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prompt_tokens_details.cache_write_tokens and completely missed the
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cache_creation_input_tokens case, so cache writes showed as 0 and reflected
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inputTokens were overstated by the cache-write amount.
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"""
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usage = SimpleNamespace(
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prompt_tokens=1000,
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completion_tokens=200,
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prompt_tokens_details=SimpleNamespace(cached_tokens=500),
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cache_creation_input_tokens=300,
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)
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normalized = normalize_usage(usage, provider="openrouter", api_mode="chat_completions")
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# Expected: cache read from prompt_tokens_details.cached_tokens (preferred),
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# cache write from top-level cache_creation_input_tokens (fallback).
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assert normalized.cache_read_tokens == 500
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assert normalized.cache_write_tokens == 300
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# input_tokens = prompt_total - cache_read - cache_write = 1000 - 500 - 300 = 200
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assert normalized.input_tokens == 200
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assert normalized.output_tokens == 200
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def test_normalize_usage_openai_reads_top_level_cache_read_when_details_missing():
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"""Some proxies expose only top-level Anthropic-style fields with no
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prompt_tokens_details object. Regression guard for cline/cline#10266.
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"""
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usage = SimpleNamespace(
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prompt_tokens=1000,
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completion_tokens=200,
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cache_read_input_tokens=500,
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cache_creation_input_tokens=300,
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)
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normalized = normalize_usage(usage, provider="openrouter", api_mode="chat_completions")
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assert normalized.cache_read_tokens == 500
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assert normalized.cache_write_tokens == 300
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assert normalized.input_tokens == 200
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def test_normalize_usage_openai_prefers_prompt_tokens_details_over_top_level():
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"""When both prompt_tokens_details and top-level Anthropic fields are
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present, we prefer the OpenAI-standard nested fields. Top-level Anthropic
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fields are only a fallback when the nested ones are absent/zero.
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"""
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usage = SimpleNamespace(
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prompt_tokens=1000,
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completion_tokens=200,
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prompt_tokens_details=SimpleNamespace(cached_tokens=600, cache_write_tokens=150),
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# Intentionally different values — proving we ignore these when details exist.
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cache_read_input_tokens=999,
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cache_creation_input_tokens=999,
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)
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normalized = normalize_usage(usage, provider="openrouter", api_mode="chat_completions")
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assert normalized.cache_read_tokens == 600
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assert normalized.cache_write_tokens == 150
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def test_openrouter_models_api_pricing_is_converted_from_per_token_to_per_million(monkeypatch):
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monkeypatch.setattr(
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"agent.usage_pricing.fetch_model_metadata",
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lambda: {
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"anthropic/claude-opus-4.6": {
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"pricing": {
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"prompt": "0.000005",
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"completion": "0.000025",
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"input_cache_read": "0.0000005",
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"input_cache_write": "0.00000625",
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}
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}
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},
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)
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entry = get_pricing_entry(
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"anthropic/claude-opus-4.6",
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provider="openrouter",
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base_url="https://openrouter.ai/api/v1",
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)
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assert float(entry.input_cost_per_million) == 5.0
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assert float(entry.output_cost_per_million) == 25.0
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assert float(entry.cache_read_cost_per_million) == 0.5
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assert float(entry.cache_write_cost_per_million) == 6.25
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def test_estimate_usage_cost_marks_subscription_routes_included():
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result = estimate_usage_cost(
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"gpt-5.3-codex",
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CanonicalUsage(input_tokens=1000, output_tokens=500),
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provider="openai-codex",
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base_url="https://chatgpt.com/backend-api/codex",
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)
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assert result.status == "included"
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assert float(result.amount_usd) == 0.0
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def test_estimate_usage_cost_refuses_cache_pricing_without_official_cache_rate(monkeypatch):
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monkeypatch.setattr(
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"agent.usage_pricing.fetch_model_metadata",
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lambda: {
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"google/gemini-2.5-pro": {
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"pricing": {
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"prompt": "0.00000125",
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"completion": "0.00001",
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}
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}
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},
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)
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result = estimate_usage_cost(
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"google/gemini-2.5-pro",
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CanonicalUsage(input_tokens=1000, output_tokens=500, cache_read_tokens=100),
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provider="openrouter",
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base_url="https://openrouter.ai/api/v1",
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)
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assert result.status == "unknown"
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def test_custom_endpoint_models_api_pricing_is_supported(monkeypatch):
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monkeypatch.setattr(
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"agent.usage_pricing.fetch_endpoint_model_metadata",
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lambda base_url, api_key=None: {
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"zai-org/GLM-5-TEE": {
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"pricing": {
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"prompt": "0.0000005",
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"completion": "0.000002",
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}
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}
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},
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)
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entry = get_pricing_entry(
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"zai-org/GLM-5-TEE",
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provider="custom",
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base_url="https://llm.chutes.ai/v1",
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api_key="test-key",
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)
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assert float(entry.input_cost_per_million) == 0.5
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assert float(entry.output_cost_per_million) == 2.0
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