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llm-provider-usage-statistics

by letta-ai

Reference guide for token counting and prefix caching across LLM providers (OpenAI, Anthropic, Gemini). Use when debugging token counts or optimizing prefix caching.

Installation

Pick a client and clone the repository into its skills directory.

Installation

Quick info

Author
letta-ai
Category
Data Science
Views
1
GitHub repo

About this skill

Reference guide for token counting and prefix caching across LLM providers (OpenAI, Anthropic, Gemini). Use when debugging token counts or optimizing prefix caching.

How to use

  1. Zainstaluj skill llm-provider-usage-statistics w swoim środowisku Letta, dodając repozytorium z gałęzi main do katalogu skills.
  2. Otwórz skill, gdy debugujesz liczenie tokenów w swoim agencie — szczególnie jeśli pracujesz z wieloma dostawcami jednocześnie.
  3. Sprawdź tabelę "Token Counting Semantics", aby zweryfikować, czy Twój dostawca wlicza cached tokeny do input_tokens czy nie. Dla Anthropic pamiętaj, że musisz ręcznie dodać cache_read_input_tokens i cache_creation_input_tokens do input_tokens.
  4. Jeśli optymalizujesz prefix caching, skonsultuj tabelę "Prefix Caching" — wszystkie trzy platformy wymagają minimum 1024 tokenów, ale OpenAI i Gemini włączają cache automatycznie, podczas gdy Anthropic wymaga jawnego ustawienia breakpoints cache_control.
  5. Dla modeli z reasoning (OpenAI o1/o3, Gemini 2.0 z thinking) sprawdź tabelę "Reasoning/Thinking Tokens", aby wiedzieć, w którym polu szukać tych tokenów w response'ie.
  6. W razie potrzeby przejdź do plików referencyjnych (openai.md, anthropic.md, gemini.md) w katalogu references, aby uzyskać szczegółowe instrukcje konfiguracji dla konkretnego dostawcy.

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