a2440f72f6
* perf: cache base_url.lower() via property, consolidate triple load_config(), hoist set constant run_agent.py: - Add base_url property that auto-caches _base_url_lower on every assignment, eliminating 12+ redundant .lower() calls per API cycle across __init__, _build_api_kwargs, _supports_reasoning_extra_body, and the main conversation loop - Consolidate three separate load_config() disk reads in __init__ (memory, skills, compression) into a single call, reusing the result dict for all three config sections model_tools.py: - Hoist _READ_SEARCH_TOOLS set to module level (was rebuilt inside handle_function_call on every tool invocation) * Use endpoint metadata for custom model context and pricing --------- Co-authored-by: kshitij <82637225+kshitijk4poor@users.noreply.github.com>
126 lines
3.7 KiB
Python
126 lines
3.7 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_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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