awesome-token

A landscape map of open source tools, paid products, and standout projects built around tokens.

awesome-token is not just a tokenizer reading list.

It is a curated ecosystem map for people who need to work with tokens in real AI systems: counting them, optimizing them, pricing them, compressing them, tracing them, and building products around them.

Contents

• What This Project Is
• How To Read This List
• Landscape
• MVP Catalog
• By Use Case
• Count and Inspect Tokens
• Estimate Cost and Pricing
• Reduce and Compress Token Usage
• Chunking, Retrieval, and Long Context
• Observe Token Usage in Production
• Developer Libraries and Foundations
• Learning and Reference
• Selection Principles
• Roadmap
• Contributing
• License

What This Project Is

This project maps the token ecosystem across three layers:

• Open source tools you can inspect, fork, self-host, or embed
• Paid product platforms that package token workflows into SaaS or commercial infrastructure
• Projects and resources that help people understand the market and build better systems

The goal is to help users answer questions like:

• What tools can count tokens today?
• Which products help teams monitor token cost in production?
• What should I use for chunking, compression, or long-context workflows?
• Which parts of the token ecosystem are open source, and which are commercial?

How To Read This List

Each entry is tagged with one of:

• Open source
• Paid product
• Project
• Docs
• Paper

This is an ecosystem map first. It favors tools and products people can actually use over broad theory dumps.

Landscape

The token ecosystem usually clusters around these user jobs:

• Counting and inspecting tokens
• Estimating cost and provider pricing
• Reducing token usage with compression or prompt optimization
• Splitting, chunking, and retrieving context
• Observing token spend, latency, and usage in production
• Building token-aware applications with SDKs and infrastructure

MVP Catalog

This repository now has a first structured catalog for commercial products:

• China Platforms Catalog
• Paid Products Catalog
• Open Source Tools Catalog

The catalog is the beginning of the platform layer. It makes products easier to compare than a flat awesome list.

By Use Case

If you only want a fast starting point, use this shortlist:

Use case	Start with
Count tokens manually	OpenAI Tokenizer, Anthropic Token Counting
Compare provider pricing	OpenAI API Pricing, Anthropic Pricing, Google AI Pricing, OpenRouter
Reduce prompt cost	LLMLingua, Selective Context
RAG chunking and long context	LangChain Text Splitters, LlamaIndex Node Parsers, Pinecone
Production token observability	Langfuse Cloud, Helicone Cloud, LangSmith, Portkey
Routing, budgets, and gateway control	OpenRouter, Portkey, Helicone Cloud

Count and Inspect Tokens

Open source

• openai/tiktoken - Open source Fast tokenizer library for OpenAI-compatible model workflows.
• huggingface/tokenizers - Open source Production-grade tokenizer toolkit and training library.
• google/sentencepiece - Open source Widely used tokenizer and detokenizer framework.
• microsoft/tiktokenizer - Open source Token counting and inspection UI for OpenAI tokenizers.

Paid products

• OpenAI Tokenizer - Paid product Provider-hosted tokenizer UI for inspecting how prompts are split into tokens.

Projects and resources

• Anthropic Token Counting - Docs Official token counting endpoint and usage docs for Claude.
• Tokenizer Playground - Project Compare tokenization behavior across tokenizers in a browser.

Estimate Cost and Pricing

Open source

• OpenAI Cookbook: How to count tokens with tiktoken - Project Practical examples for estimating prompt size before making API calls.

Paid products

• OpenAI API Pricing - Paid product Official pricing reference for token-billed OpenAI APIs.
• Anthropic Pricing - Paid product Official pricing reference for Claude APIs.
• Google AI Pricing - Paid product Official pricing reference for Gemini APIs.
• OpenRouter - Paid product Multi-provider model routing platform useful for comparing access and token economics across providers.
• Portkey Pricing - Paid product AI gateway platform with cost visibility, budgets, and provider-aware pricing controls.

Projects and resources

• OpenAI Help: What are tokens and how to count them? - Docs Introductory explanation connecting token counts and usage cost.

Reduce and Compress Token Usage

Open source

• microsoft/LLMLingua - Open source Prompt compression methods for reducing cost and latency.
• liyucheng09/Selective_Context - Open source Context compression approach for LLM prompts.

Paid products

• PromptLayer - Paid product Prompt management and observability platform with token and cost visibility.
• Portkey - Paid product AI gateway and prompt layer with observability, routing, and cost controls.

Projects and resources

• LangSmith Cost Tracking - Docs Documentation for attaching token usage and cost data to traces.

Chunking, Retrieval, and Long Context

Open source

• LangChain Text Splitters - Open source Common chunking strategies used in RAG pipelines.
• LlamaIndex Node Parsers - Open source Chunking and parsing tools for indexing workflows.
• THUDM/LongBench - Open source Benchmark for long-context LLM behavior.
• NVIDIA/RULER - Open source Synthetic benchmark suite for long-context evaluation.

Paid products

• Pinecone - Paid product Vector database platform frequently used in token-sensitive retrieval and chunking workflows.

Projects and resources

• Chunking Strategies for LLM Applications - Docs Practical guide to chunk size tradeoffs and retrieval design.
• Lost in the Middle: How Language Models Use Long Contexts - Paper Foundational paper on long-context retrieval behavior.
• Anthropic: What We Look At When We Look At Context Windows - Docs Practical notes on how long-context prompting behaves in practice.

Observe Token Usage in Production

Open source

• langfuse/langfuse - Open source LLM engineering platform with open source observability and token/cost tracking.
• Helicone/helicone - Open source Open source LLM observability stack with token and cost analytics.

Paid products

• Langfuse Cloud - Paid product Managed observability platform with token and cost tracking features.
• Helicone Cloud - Paid product Managed gateway and observability product with cost and token monitoring.
• LangSmith - Paid product Observability platform for LLM applications with tracing and usage analysis.
• PromptLayer - Paid product Managed prompt and tracing platform with production visibility.
• Portkey - Paid product Gateway and control plane with observability, cost management, and budgeting.
• Braintrust - Paid product Observability and evaluation platform with token and cost visibility in production traces.

Projects and resources

• Helicone Cost Tracking - Docs Practical guide to tracking model cost and token usage with Helicone.
• LangSmith View Usage - Docs Usage and billing visibility in LangSmith.
• Portkey Cost Management - Docs Official cost management docs covering token-based budgets and pricing tracking.
• Braintrust Observability - Docs Official docs on tracing model calls, token counts, and estimated costs.

Developer Libraries and Foundations

Open source

• openai/tiktoken - Open source Fast tokenizer implementation used across many OpenAI-based systems.
• huggingface/tokenizers - Open source Low-level tokenizer toolkit for training and inference workflows.
• google/sentencepiece - Open source Foundation library behind many tokenizer pipelines.
• langchain-ai/langchain - Open source Framework commonly used for token-aware splitting and prompt assembly.
• run-llama/llama_index - Open source Framework for retrieval and indexing pipelines where chunking and token limits matter.

Paid products

• LangSmith Pricing - Paid product Commercial platform built around tracing, evaluation, and usage workflows for LLM systems.
• Braintrust Plans and Limits - Paid product Pricing and limits for Braintrust observability and eval workflows.

Projects and resources

• OpenAI Cookbook - Docs Broad set of practical examples, including token budgeting and prompt handling.

Learning and Reference

Projects and resources

• Hugging Face Course: Tokenizers - Docs Beginner-friendly introduction to tokenizers and subword methods.
• Let's build the GPT Tokenizer - Project Strong practical walkthrough of BPE concepts.
• Byte Pair Encoding is Suboptimal for Language Model Pretraining - Paper Research on tokenization limitations.
• A Formal Perspective on Byte-Pair Encoding - Paper More theoretical perspective on BPE behavior.

Selection Principles

This project prefers resources that are:

• Directly useful to people with token-related workflows
• Primary-source whenever possible
• Clearly open source, commercial, or reference-oriented
• Relevant to a real user job, not just generally adjacent to AI

This project avoids:

• Weakly related generic AI directories
• Thin wrappers with no clear value
• Hype-heavy lists without practical signal
• Low-quality duplicates

Roadmap

• Expand each scene with stronger commercial product coverage
• Add a By Use Case section for chatbots, RAG, agents, evaluation, and infra teams
• Add structured metadata for entries such as Pricing, Deployment, and Best for
• Evolve from a curated list into a browsable token ecosystem index

Contributing

Please read CONTRIBUTING.md before submitting a pull request.

The highest-value additions right now are:

• Strong open source tools with real usage
• Paid products with a clear token workflow
• High-signal projects around token cost, chunking, or observability
• Better ecosystem coverage across providers and product categories

License

MIT