llm-usage 0.1.4

LLM usage tracker with cost/latency logging, fallback, and a web dashboard

llm-usage

A lightweight toolkit to call OpenAI and Gemini models while automatically logging usage (tokens), costs, latency, errors/retries, streaming diagnostics, and more — plus a built-in dashboard for browsing, filtering, rerunning requests, and inspecting inputs/outputs.

Features

• Provider-agnostic logging across OpenAI and Gemini
• Per-call metadata: tags, parameters (temperature, top_p, max tokens), model/provider
• Token accounting: input, cached input, output, reasoning tokens (when available)
• Cost computation: per-million token pricing with cached input pricing
• Latency metrics: total latency, time-to-first-token (TTFT), stream duration, chunk stats
• Errors/retries and cross-provider fallback support
• API-key-aware fallback within provider steps
• Streaming support for both providers with live console printing
• SQLite storage with an inspectable schema
• Beautiful dashboard (FastAPI + Jinja2): filtering, totals, details, input/output view, and rerun
• Simple Python API and CLI commands

Install

pip install llm-usage

From source (editable):

pip install -e .

Environment

• OPENAI_API_KEYS: comma-separated OpenAI keys (e.g. sk-aaa,sk-bbb)
• GEMINI_API_KEYS: comma-separated Gemini keys (e.g. g-aaa,g-bbb)
• LLM_LOG_DIR: directory to store logs/SQLite (default: ./logs)

CLI

• Start the dashboard:

llm-usage-dashboard
# then open http://127.0.0.1:8000

• Run sample calls:

llm-usage-samples

Python API

Import the main APIs from the package:

from llm_usage import LLM, CallParameters

llm = LLM(
  openai_api_keys=["sk-..."],
  gemini_api_keys=["g-..."],
  log_dir="./logs",
)

res = llm.call_model(
  model="gpt-5-mini",
  input_or_contents="Write a two-line poem about monsoons.",
  parameters=CallParameters(temperature=0.2, max_tokens=200),
  api_key_alias="openai-0",
  tags=["demo", "poem"],
)
print(res["output_text"])  # response string

Streaming

res = llm.call_stream_model(
  model="gemini-2.5-flash",
  input_or_contents=["Explain how AI works"],
  instructions="Stream the response.",
  api_key_alias="gemini-0",
  tags=["demo", "stream"],
)
# chunks print live to stdout; res includes final request_id/call_id

Generation parameters (temperature, top_p, max tokens)

from llm_usage import CallParameters

# OpenAI example
res = llm.call_model(
  model="gpt-5-mini",
  input_or_contents="Write a two-line limerick about rain.",
  parameters=CallParameters(temperature=0.2, top_p=0.9, max_tokens=120),
  api_key_alias="openai-0",
  tags=["demo", "params"],
)
print(res["output_text"]) 

# Gemini example (temperature and max tokens are supported via config)
res = llm.call_model(
  model="gemini-2.5-flash",
  input_or_contents="Summarize monsoons in one paragraph.",
  parameters=CallParameters(temperature=0.3, max_tokens=180),
  api_key_alias="gemini-0",
  tags=["demo", "params"],
)
print(res["output_text"]) 

Gemini thinking and thought summaries

Gemini 2.5 models support a thinking budget and optional thought summaries. You can reduce latency by setting thinking_budget=0, or enable summaries with include_thoughts=True.

# Disable thinking for faster responses
res = llm.call_model(
  model="gemini-2.5-flash",
  input_or_contents="How does AI work?",
  api_key_alias="gemini-0",
  thinking_budget=0,               # disable thinking
  tags=["demo", "thinking"],
)

# Enable thought summaries (returned tokens counted in usage)
res = llm.call_model(
  model="gemini-2.5-pro",
  input_or_contents="What is the sum of the first 50 prime numbers?",
  api_key_alias="gemini-0",
  thinking_budget=-1,              # dynamic
  include_thoughts=True,
  tags=["demo", "thinking"],
)
print(res["output_text"]) 

Structured outputs

Use structured_schema to request typed outputs. Define Pydantic models and pass them with your call. The parsed result is returned in res["output_parsed"].

OpenAI example:

from pydantic import BaseModel

class CalendarEvent(BaseModel):
  name: str
  date: str
  participants: list[str]

res = llm.call_model(
  model="gpt-5-mini",
  input_or_contents=[
    {"role": "system", "content": "Extract the event information."},
    {"role": "user", "content": "Alice and Bob are going to a science fair on Friday."},
  ],
  structured_schema=CalendarEvent,
  api_key_alias="openai-0",
  tags=["demo", "structured"],
)
print(res["output_parsed"])  # CalendarEvent(...)

Gemini example:

from pydantic import BaseModel

class Recipe(BaseModel):
  recipe_name: str
  ingredients: list[str]

res = llm.call_model(
  model="gemini-2.5-flash",
  input_or_contents="List a few popular cookie recipes, and include the amounts of ingredients.",
  structured_schema=list[Recipe],   # or Recipe for single-object outputs
  api_key_alias="gemini-0",
  tags=["demo", "structured"],
)
print(res["output_parsed"])  # List[Recipe]

Note: On Gemini, Pydantic validation errors may be suppressed by the underlying client; .parsed may be empty/null if the output cannot be validated. The raw JSON string is still available via res["output_text"].

Gemini URL context tool

To allow Gemini to fetch and ground on URLs mentioned in your prompt, enable the built-in URL context tool by setting url_context=True:

res = llm.call_model(
  model="gemini-2.5-flash",
  input_or_contents="What are the top 3 recent announcements from the Gemini API according to https://ai.google.dev/gemini-api/docs/changelog",
  api_key_alias="gemini-0",
  url_context=True,
  thinking_budget=0,
  tags=["demo", "url-context"],
)
print(res["output_text"]) 

Fallback across models/providers

Provide a fallback plan as a list of steps with model and optional key aliases. Each step is attempted in order; on failure, it proceeds to the next.

plan = [
  {"model": "gpt-5-mini", "key_aliases": ["openai-0", "openai-1"]},
  {"model": "gemini-2.5-pro", "key_aliases": ["gemini-0"]},
]
res = llm.call_model(
  model="gpt-5-mini",
  input_or_contents="Say 'fallback test ok'",
  tags=["demo", "fallback"],
  fallback_plan=plan,
)
print(res)  # { success, request_id, call_id }

Dashboard

The dashboard reads from the SQLite DB in LLM_LOG_DIR/llm.sqlite.

• Filters: provider, model, tag, success, date range
• Summary: total requests, total cost, average latency
• Table: time, provider, model, tags, latency, tokens, cost, status, details link
• Details: pretty metadata JSON, input and output text, latencies, tokens, costs, rerun button

Data model (high level)

• Request record fields (subset):
  • identifiers: request_id, call_id, timestamps
  • classification: provider, model, api_key_alias, tags
  • tokens: input_tokens, cached_input_tokens, output_tokens, reasoning_tokens
  • costs: input_cost_usd, cached_input_cost_usd, output_cost_usd, reasoning_cost_usd, total_cost_usd
  • latency: latency_ms, ttft_ms, stream_duration_ms, stream chunk stats
  • results: response_text, response_text_len, finish_reason, success
  • errors/rate limit: error_json, rate_limit_json, retry_count

Pricing notes

• Costs are calculated per 1M tokens (USD) using a built-in pricing table derived from your config in src/utils/llm-models.py.
• Billed formula examples:
  • OpenAI: uncached_input * input_rate + cached_input * cached_rate + output * output_rate
  • Gemini (Responses API): output = candidates + thoughts; cached input billed separately

Tips

• API key aliases are created automatically (openai-0, gemini-0, ...). You can specify which alias to use per call.
• For streaming, the SDK prints chunks to stdout as they arrive; the final usage is recorded in the database.
• Each direct call gets a generated call_id. Fallback attempts share the same call_id for aggregation.