AI · alternatives · 2026

24 alternatives to Tokensift, an open-sourced token-efficiency linter for LLM prompts

Token-efficiency linter for LLM prompts and payloads - ritenv/tokensift

Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes.

1AS
A surprisingly effective way to predict token importance in LLM prompts▲14
We explored a novel method to gauge the significance of tokens in prompts given to large language models, without needing direct model access. Essentially, we just did an ablation study on the prompt using cosine similarity of the embeddings as the measure. We got surprisingly promising results when comparing this really simple approach to integrated gradients. Curious to hear thoughts from the community!
2023 · heatmap.demos.watchful.io · its alternatives →
2
TokenDagger – A tokenizer faster than OpenAI's Tiktoken▲281
TokenDagger is a drop-in replacement for OpenAI’s Tiktoken (the tokenizer behind Llama 3, Mistral, GPT-3.*, etc.). It’s written in C++ 17 with thin Python bindings, keeps the exact same BPE vocab/special-token rules, and focuses on raw speed. I’m teaching myself LLM internals by re-implementing the stack from first principles. Profiling TikToken’s Python/Rust implementation showed a lot of time was spent doing regex matching. Most of my perf gains come from a) using a faster jit-compiled regex engine; and b) simplifying the algorithm to forego regex matching special tokens at all.…
2025 · github.com · its alternatives →
3
Tokenwise▲143
A smart LLM proxy that shows where you're overpaying
Jun 2026 · tokenwisehq.com · its alternatives →
4
`tc` like `wc` but for LLM tokens▲8
Little tool that I made to understand how (un)reasonable my prompts are.
Jan 2026 · github.com · its alternatives →
5AT
A tool to benchmark LLM APIs (OpenAI, Claude, local/self-hosted)▲55
I recently built a small open-source tool to benchmark different LLM API endpoints — including OpenAI, Claude, and self-hosted models (like llama.cpp). It runs a configurable number of test requests and reports two key metrics: • First-token latency (ms): How long it takes for the first token to appear • Output speed (tokens/sec): Overall output fluency Demo: https://llmapitest.com/ Code: https://github.com/qjr87/llm-api-test The goal is to provide a simple, visual, and reproducible way to evaluate performance across different LLM providers, including…
2025 · llmapitest.com · its alternatives →
6
Prompt-refiner – Lightweight optimization for LLM inputs and RAG▲7
Hi HN, While building RAG agents, I noticed a lot of token budget was wasted on formatting overhead (HTML tags, JSON structure, whitespace). Existing solutions felt too heavy (often requiring torch/transformers), so I wrote this lightweight, zero-dependency library to solve it. It includes strategies for context packing, PII redaction, and tool output compression. Benchmarks show it can save ~15% of tokens with negligible latency overhead (<0.5ms). Happy to answer any questions!
Dec 2025 · github.com · its alternatives →
7LT
LLM Token Visualizer – How big is 128k token input▲6
2025 · v0-llm-token-visualizer.vercel.app · its alternatives →
8
TokenCrush▲11
Thrive in a world of increasing LLM costs
2025 · tokencrush.ai · its alternatives →
9
LLMs can be susceptible to a new kind of malware▲17
New ways of breaking app-integrated LLMs . Contribute to greshake/llm-security development by creating an account on GitHub.
2023 · github.com · its alternatives →
10
An unmetered LLM API–$6/month, no token tracking, no limits▲12
Hi HN, I was once given the advice: Don't waste expensive frontier model credits (GPT/Claude/etc.) on bulk work. Send the boring, repetitive, high-volume jobs to a smaller model, and save the expensive prompts for when you actually need frontier-level reasoning. I complained and told my manager that I shouldnt have to think about using certain models for certain coding tasks, and that one model should handle everything. Well, here we are anyway. If anyone needs a place to absolutely abuse an LLM with high-volume tasks, come beat ours up at https://yolo-auto.com. Here are…
Jul 2026 · yolo-auto.com · its alternatives →
11
prompt efficiency suite▲7
hi. i'm dulra. i strongly believe in moving humanity forward. we're at a tipping point: ai-driven applications are generating hundreds of millions of prompts daily, costing organizations millions in token fees and creating governance nightmares. every wasted word compounds at scale—across chatbots, summarizers, code assistants, and virtual agents—making prompt efficiency not just a nice-to-have, but a strategic imperative. by treating prompts as first-class assets, we can drive down llms usage costs by up to 60%, eliminate compliance risks, and unlock new multi-model workflows that keep us…
2025 · prompt-efficiency-suite.lovable.app · its alternatives →
12
Keep large tool output out of LLM context: 3x accuracy 95% fewer tokens▲10
LLM agents often place raw JSON tool outputs directly in the prompt. After a few tool calls, earlier results get compacted or truncated and answers become incorrect or inconsistent. I built Sift, a drop-in MCP gateway that stores tool outputs as local artifacts (filesystem blobs indexed in SQLite) and returns an `artifact_id` plus compact schema hints when responses are large or paginated. Instead of reasoning over full JSON in the prompt, the model runs a small Python query: def run(data, schema, params): return max(data, key=lambda x: x["magnitude"])["place"] Query code runs in a…
Mar 2026 · github.com · its alternatives →
13
I open-sourced a $1M engine for closing loops in embedding space▲8
Hey folks, I’m the creator of WFGY — a semantic reasoning framework for LLMs. After open-sourcing it, I did a full technical and value audit — and realized this engine might be worth $8M–$17M based on AI module licensing norms. If embedded as part of a platform core, the valuation could exceed $30M. Too late to pull it back. So here it is — fully free, open-sourced under MIT. --- ### What does it solve? Current LLMs (even GPT-4+) lack *self-consistent reasoning*. They struggle with: - Fragmented logic across turns - No internal loopback or self-calibration - No modular thought units - Weak…
2025 · github.com · its alternatives →
14HP
Horizon – Programmatic Prompt Generation and LLM Configurations▲7
Hi HN. I heard you like dev tools and AI, so we wanted to share our project that we’ve been working on. We’re working on Horizon [1] - a higher level abstraction for LLMs so that developers can spend less time trying to grapple with LLMs to make them work and more time with users. This is the starting feature set which takes an auto-ML approach to identify the optimal LLM model, hyperparameters, and prompt - instead of just giving you the tooling to figure it out yourself. You can read more about it in our documentations. Our view is that as LLMs become increasingly commoditized and prompts…
2023 · gethorizon.ai · its alternatives →
15
PromptFlow – Low-Code GUI Tool to Chain LLM Prompts with Flowcharts▲7
2023 · github.com · its alternatives →
16LA
Lightmod, an all-in-one tool for full stack Clojure▲7
2017 · sekao.net · its alternatives →
17IB
I Built a UI to finetune LLMs x100 faster▲7
After fine-tuning GPT for a personal project, I realized how tedious it is to write plain text in a massive JSON file. That's why I built this app for my own use, and I want to see if others could benefit from a tool like this as well ;)
2024 · finetuna-ui.com · its alternatives →
18
I built an open-source “unit testing” suite for prompts ⮂ inputs▲5
Hi HN, I'm pleased to share Promptspot, an open-source (Apache License 2.0) project that helps automate testing of large language model (LLM) prompts against an array of input data. Modern LLMs offer an enormous amount of leverage if you "teach the bot to fish" — i.e. simply prompt it with both a "system prompt" (which typically doesn't change often) and a dynamic input, which is often application state, search results, recent activity, user profile data, etc. Existing playgrounds and prompt management systems often lack the rigor and flexibility required for this dynamic approach — and as…
2023 · github.com · its alternatives →
19
LLMSlim v0.3.0▲4
Cut LLM token costs 40-70% with offline prompt compression
Jul 2026 · llmslim.app · its alternatives →
20OS
OpenSkills – Stop bloating your LLM context with unused instructions▲9
Hello HN, I’ve been building AI agents lately and ran into a common "Context Bloat" problem. When an agent has 20+ skills, stuffing every system prompt, reference doc, and tool definition into a single request quickly hits token limits and degrades model performance (the "lost in the middle" problem). To solve this, I built OpenSkills, an open-source SDK that implements a Progressive Disclosure Architecture for agent skills. The Core Concept: Instead of loading everything upfront, OpenSkills splits a skill into three layers: Layer 1 (Metadata): Light-weight tags and triggers (always loaded…
Jan 2026 · its alternatives →
21CO
Coast on Clojure: A full stack framework with a catchy name▲8
2017 · medium.com · its alternatives →
22IA
Implementing a Lispy Language in Haskell: Part 1▲6
Software Engineer in New York specializing in Apple platforms
2015 · harlanhaskins.com · its alternatives →
23
Rust Based Per-Token Late Interaction Dense Search▲8
Happy to release FastPlaid, which aim to ease and accelerate ColBERT and ColPali retrieval
2025 · github.com · its alternatives →
24
ICortex: LLM Powered Python Interpreter▲8
A no-code development framework—Let AI do the coding for you 🦾 - textcortex/icortex
2023 · github.com · its alternatives →

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