Hitting the Claude limit? Four tools, four different angles
These are the links promised in the reel. All four are open source, all four are free.
Links were checked on 2026-08-31. All four were live.
Why the limit runs out
- 01The model re-reads the whole conversation for every question.
- 02Tool results, logs and JSON output stay inside that conversation.
- 03The agent starts learning the codebase from scratch in every new session.
- 04Code you never asked for gets written, and then that code enters the context too.
So the limit is eaten from four different places. Each tool below closes one of them. That is why the reel lists all four together.
Compresses before it reaches Claude
Sits between you and the model and compresses unnecessary content before it ever reaches Claude. Works as a library, a proxy or an MCP server.
uv tool install --python 3.13 "headroom-ai[all]"pip install "headroom-ai[all]"Either command is enough. The uv route builds an isolated environment for the tool, so your system Python stays untouched.
The number
Its README promises 60-95% fewer tokens on JSON data and 15-20% fewer on coding agents. That is the tool's own claim, we did not measure it.
When you need it
When tool results, logs and JSON responses are long. That is, when half the conversation is machine output.
Careful. GitHub hosts several projects with the same name. The compression one is the address above.
Reads the code once, not every time
Turns a codebase into a knowledge graph so Claude does not re-read the code every session. The graph lives in a file and survives the session.
pip install graphifyy && graphify installThe package is named graphifyy, with two y letters. The command is still graphify. On macOS use pipx install graphifyy if pip fails.
The number
Its README states 71.5x fewer tokens per query compared to reading the raw files. That is the tool's own claim. Our own measurement is in the section below.
When you need it
When the project is large and the same questions keep coming back.
A bank statement for your spend
Shows where your AI coding spend goes. Think of it as a bank statement: where the money went, which model, which project.
npx codeburnnpm install -g codeburnThe npx option runs it once without installing anything. Use the second command for permanent use.
The number
We publish no savings percentage here, because this tool measures, it does not save. Its value is that only it can show what the other three actually did.
When you need it
Install this one first. Without knowing what you spend, you cannot know what the other three gave you.
Code never written burns no tokens
Pushes the agent to write less code. The cheapest token is the one never written, so this tool attacks the problem from a different side than the other three.
/plugin marketplace add DietrichGebert/ponytailThis command is typed inside Claude Code, not in a terminal. The repo lists separate lines for Codex and Copilot.
The number
Its README states roughly 54% less code, 20% cheaper and 27% faster, measured across 12 tasks with Haiku 4.5, n=4. That is the tool's own benchmark, we did not reproduce it.
When you need it
When the agent starts building things you never asked for.
Careful. The reel ran out of time before this tool, so everything here comes from the repo README, not from our own use.
We measured this on our own codebase
We built a graph of our own Instagram project. 164 files, roughly 568 thousand words.
The one-off cost was 1,248,812 input tokens. 15 agents ran, averaging 83,254 tokens each.
The result: 2222 nodes, 4862 edges, 125 communities. 92% of the extraction came straight from the text, 7% was inferred.
| Total, 15 agents | 1,248,812 token |
| Average per agent | 83,254 token |
| 1 image, in its own agent | 74,979 token |
| 8 images, in one agent | 79,685 token |
| 6 boilerplate SVGs, result 6 nodes | 67,704 token |
| 15 documents, result 107 nodes | 186,296 token |
An honest note: 1,248,812 is the real sum across all 15 agents. A figure of 1,102,516 was visible for a while; it was a hand typed mistake made during the merge step and has been corrected.
Three things that did not work
- A single vendored library file took up 32% of the graph, 721 nodes. None of them meant anything.
- 6 boilerplate SVG files cost 67,704 tokens and produced 6 nodes in total.
- A single image in its own agent yields 15-26 nodes. Batch 8 images into one agent and you get 1 node per image. Cheap, but empty.
The rule: the price is set by the number of agents, not by file size.
When none of this helps
- If you do not write code, none of these tools are for you.
- On a small project the graph never pays for itself. The one-off cost outgrows the questions.
- The graph is static. It goes stale when the code changes and has to be rebuilt.
- If your limit comes from an hourly cap or a plan tier rather than tokens, compression will not lift it.
- Three of the four numbers are the tools' own claims. We only measured Graphify on our own codebase.
Frequently asked questions
Do these tools cost money?+
All four are open source and distributed free on GitHub. The model subscription is separate, and these tools do not replace it.
Can the Claude limit be raised directly?+
No. These tools do not raise the limit. They let you do more work within the same limit, which is a different thing.
Which one should I install first?+
CodeBurn. It is the one that measures spend. Without the baseline number you cannot measure what the other three gave you.
Why is the Graphify package spelled with two y letters?+
The name graphify is still taken on PyPI, so the package ships as graphifyy. After installing, the command itself is still graphify.
Do I have to install all four?+
No. Each one works on its own. The reel lists four because each addresses a different side of the limit problem.
Is this page the official documentation?+
No. The official documentation is each tool's own GitHub page. What you get here is the links, the install commands and our own measurement.
Whoever hides the number is ashamed of it
If you read this page and decided it is not for you, one thing remains.
We published the 1.2 million tokens our own graph cost. What it earned, and what it turned into noise.
The team that builds your website should report the same way. How many pages, how many days, what did not work.
Baypro says: whoever hides the number is ashamed of it.
Do you know which page of your site actually brings you customers?
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