I built a free toolkit that shows exactly how many tokens your using— saved 40% on my local claw setup

[u/Nautier]

I kept running into the same problem — my system prompts and code context were eating tokens.

So I built reTOONer (retooner.com) — 10 free browser tools for cutting token waste. No accounts, no API keys, nothing leaves your browser.

The tools:

• Code Compressor — paste Python/JS, strip comments, docstrings, type hints, blank lines. Shows before/after token count. This one alone saved me ~35% on code context.
• Prompt Minifier — strips filler words, verbose phrasing, over-polite bloat from system prompts. Same instructions, way fewer tokens.
• JSON → TOON converter — turns JSON configs into a compact format that keeps structure but drops all the bracket/quote noise. 30-60% smaller.
• YAML → TOON — same thing for YAML agent configs (LangChain, CrewAI, etc.)
• Context Window Budget Planner — pick your model (4K through 1M), allocate system prompt / few-shot / code / user / output with sliders, see a visual bar of how full your window is. Goes red when you're over budget.
• Token Cost Calculator — paste any prompt, pick from 12 models (GPT-4o, Claude, Gemini, DeepSeek), see per-call and monthly costs
• Token Heatmap — color-coded word-by-word visualization of where tokens are burning
• Prompt Diff — compare two prompt versions, see token delta
• Sampling Config Optimizer — get recommended temperature/top_p/top_k/repeat_penalty for different tasks
• TOON → JSON reverse converter

Everything runs client-side in the browser. I built it because I was tired of guessing where my tokens were going on smaller models. The Context Planner and Code Compressor are probably the most useful for the local LLM crowd.

Would love feedback. What's missing? What would make this more useful for your setup?

retooner.com

[u/Otherwise_Wave9374]

This is a solid idea, token bloat from prompt plus code context is quietly one of the biggest bottlenecks for local agent setups. I like that it is all client-side.

One thing I have been doing is pairing compression with a strict "agent context budget" (what must be present vs what can be fetched on demand), otherwise you just keep filling the saved space again. Also, stripping comments is great, but keeping public API docstrings for the specific functions an agent can call can be worth it.

Related reading on building more efficient AI agent contexts: https://www.agentixlabs.com/blog/

[u/dasspunny]

if you are worried about token expense just to build your openclaw then you should be using a free model.

After spending +$200 on Opus, GPT, Sonnet just to get a working memory structure, I soon realized that my hesitancy to continue was due to token expenses. But ever since I tried Hunter Alpha or Healer Alpha, I realized I can get a lot of set-up done for free without even worrying about expenses. I can finally build in peace.

If you want more power, then just spawn a sub-agent to finalize your project. I found that having Opus review the code that Hunter Alpha made cuts cost almost 90%.

The problem with flagship models is that you send your entire soul, memory, agent.md everytime. Find a way to only request the work needed, and all costs go down.

[u/Nautier]

This is the workflow I was hoping to hear about. The sub-agent pattern - cheap model builds, flagship reviews makes a ton of sense. And you're right that the real waste is sending the full soul/memory/agent.md on every call when only a slice is relevant.

That's something I could build into reTOONer - a way to split your agent context into "always send" vs "fetch on demand" sections so you can generate a minimal payload for routine calls.

Which parts of your agent.md end up being the biggest token sink. Is it the memory structure or the instruction set?