llmtrim

Name: llmtrim
Author: fkiene

by fkieneversion 0.13.4i★ –iRuns on your computer

Registry nameio.github.fkiene/llmtrim

Install

Overview

Description from the maker (original English)

MCP server and proxy that compresses LLM prompts, tool output, and replies to cut token cost.

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
UsageiNot measured yet★ –

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

Connection: Runs on your computer
Transport: stdio
Category: Search & AI memory

Install

Pick the AI app you use, then copy the settings.

Before you install

Basic checks: 1 of 1 passed
Runs on your computer with your user’s permissions (files, network, apps).
The registry lists no key to fill in.

You need

npmNode.js installed (for the npx command)

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "llmtrim": {
      "command": "npx",
      "args": [
        "-y",
        "@llmtrim/cli",
        "mcp"
      ]
    }
  }
}

Run this in your terminal

claude mcp add llmtrim -- npx -y @llmtrim/cli mcp

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "llmtrim": {
      "command": "npx",
      "args": [
        "-y",
        "@llmtrim/cli",
        "mcp"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "llmtrim": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "@llmtrim/cli",
        "mcp"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 7, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account fkiene, which owns the repo
… Not checked yet
Still maintained
We have not read this repo from GitHub yet
… Not checked yet
Has a licence
We have not read this repo from GitHub yet
– Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
– Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
… Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Search & AI memoryi

More stars than 0% of the other 7258 servers in this category
Published to the registry more recently than 39% of the other 7258 servers in this category
Basic checks equal to or better than 100% of the other 7258 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is llmtrim safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (0.13.4) reached the registry 2 months ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Node.js on your computer.

What can I use instead?

In the same category: Context7, Codebase Memory, Exa

Alternatives

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Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

Context7

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Feeds AI the latest docs for libraries and frameworks before it answers, so code it writes does not use outdated, removed functions.

Computer or cloud★ 64,224MITUpdated today

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Codebase Memory

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Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.

Runs on your computer★ 48,148MITUpdated 20 days ago

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Exa

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Connect AI agents to Exa for web search, content fetching, and multi-step research.

Runs in the cloud★ 5,213MITUpdated last month

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README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

llmtrim

Local proxy that compresses LLM API traffic so you pay less. Same answers, smaller bill.

−31% input · −74% output · −66% round-trip cost · 112 live A/B cases · ~5 ms/call · no model to load

Using Claude Code ? One install also gets you /sub to serve it through CLIProxyAPI.

Proxy · CLI · MCP · library (Python · Ruby · Swift · Kotlin · JS/WASM)

What it does • Install • Day to day • In action • Works with • Claude Code • Numbers • Config • CLI & library

What it does

You run Claude Code, Codex, Cursor, or your own app. Every turn, the tool sends a large request: system prompt, tools, history, raw command output. You pay for every token of that, including the parts that do not help the model.

A 200-line build log with two errors. Tool schemas resent on every call. JSON with hundreds of near-identical rows. That bulk is still billed.

llmtrim sits on your machine as a local proxy, trims the waste, and forwards a smaller request. The reply is unchanged. You keep the same tools and answers; you spend less.

  before:  your tool ───── full request ─────▶  OpenAI / Anthropic / …

◀──────── reply ──────────

after: your tool ──▶ llmtrim ──smaller──▶ OpenAI / Anthropic / … (on your machine) ◀──────── reply ────────── (same answer)

Compression cannot raise your bill or break a request; worst case is zero savings. Everything runs locally, nothing is sent to us. In action →

For Claude Code the same install also wires /sub to route sessions through another subscription. Details →

Get started

npm install -g @llmtrim/cli@latest && llmtrim setup

open a new terminal, then keep working

llmtrim status

That's it. setup starts a local proxy and wires your shell. When Claude Code is present, it also turns on /sub. You do not run a separate install for that.

You want	Run
First install	`llmtrim setup`
New version	`llmtrim update` (then `llmtrim ensure` after npm/brew/cargo)
Something broken	`llmtrim ensure` · `llmtrim doctor --fix` · or `f` in `status`

Any tool that honors HTTPS_PROXY works (Claude Code, Codex, Cursor, Aider, your SDK). GitHub Copilot does not (certificate pinning). Full list →

Other installers (Homebrew, curl, Scoop, Cargo, Docker)

# Linux / macOS

curl -fsSL https://raw.githubusercontent.com/fkiene/llmtrim/main/install.sh | sh

Windows (PowerShell)

irm https://raw.githubusercontent.com/fkiene/llmtrim/main/install.ps1 | iex

Package managers

brew install fkiene/tap/llmtrim cargo binstall llmtrim scoop install llmtrim docker run -d -p 43117:43117 -v llmtrim-state:/data ghcr.io/fkiene/llmtrim

Full options: INSTALL.md.

Desktop tray (menu bar / system tray)

Menu-bar / system-tray popover with the same savings numbers. Bundled in Homebrew, Scoop, and npm; setup can enable open-at-login. Open with llmtrim tray. On Linux desktops, interactive ensure can fetch the tray binary from the latest release (needs libwebkit2gtk-4.1 and libayatana-appindicator3).

Is this safe?

Same technique as mitmproxy, scoped to LLM API hosts only. setup changes three things; llmtrim uninstall reverses all three:

Private CA in ~/.llmtrim/ (name-constrained; cannot intercept your bank or email)
Shell env: HTTPS_PROXY + CA trust
Login service: daemon at login

No API keys stored (your tool's auth is forwarded). Prompts never touch disk; only anonymous token counts. Recoverable tool results stay in bounded daemon RAM for five hours by default and disappear on restart. Full threat model: SECURITY.md.

llmtrim ca

openssl x509 -in ~/.llmtrim/ca.pem -noout -text | grep -A3 "Name Constraints"

Day to day

llmtrim status     # savings + health  (aliases: monitor, gain)

llmtrim update # new release, restart daemon, refresh integrations llmtrim ensure # match the recommended install state on this machine

Situation	Command
Watch savings	`llmtrim status`
After `npm` / `brew` / `cargo` upgrade	`llmtrim ensure` (or `f` in status)
Diagnose	`llmtrim doctor` · repair with `doctor --fix`
Pause / resume proxy	`llmtrim stop` · `llmtrim start`
Force one session through llmtrim	`llmtrim wrap claude`
Remove everything	`llmtrim uninstall`

After setup, update, or ensure, owned Claude Code /sub stays in sync with the binary.

Time series: llmtrim status --daily · --weekly · --monthly · --json · --csv.

In action

An agent ran a build. The tool returned 58 lines; two were errors. All 58 would have been billed.

4,662 chars → 978 (−79%). Errors stay verbatim. Repeated INFO lines fold into a template plus the values (lossless when the range is regular).

# before (noise + signal)

[2026-06-13T10:02:00Z] INFO compiling module core::worker::task_0 (incremental) … 28 more near-identical INFO lines … [2026-06-13T10:02:31Z] ERROR src/worker/pool.rs:214: mismatched types: expected usize, found i64 … 25 more INFO lines … [2026-06-13T10:03:01Z] ERROR src/net/conn.rs:88: cannot borrow buf as mutable more than once [2026-06-13T10:03:02Z] INFO build failed, 2 errors

after (errors verbatim; INFO folded losslessly)

[{}] INFO compiling module core::worker::task_{} (incremental) [×30: (10:02:00Z..10:02:29Z step 1s; 0..29)] [2026-06-13T10:02:31Z] ERROR src/worker/pool.rs:214: mismatched types: expected usize, found i64 [{}] INFO compiling module core::net::conn_{} (incremental) [×25: 10:02:32Z..10:02:56Z; 0..24] [2026-06-13T10:03:01Z] ERROR src/net/conn.rs:88: cannot borrow buf as mutable more than once [2026-06-13T10:03:02Z] INFO build failed, 2 errors

Try it on a request body of your own:

echo '{"model":"gpt-4o","messages":[...]}' | llmtrim compress --provider openai

Log folding is one stage. Others kick in on different waste:

Waste	What happens
Build logs, diffs, grep dumps	Keep errors / changes / matches; fold the rest
Long pasted context	Keep chunks relevant to the question
Source code	Keep useful bodies; rest → signatures
Tool schemas every turn	Trim + keep the cache prefix stable
Huge JSON arrays	Compact table (TOON) or sample
Verbose model replies	Ask for terser output where safe

[!IMPORTANT]

Compression cannot raise your bill or break a request. Each stage is re-measured with the provider's real tokenizer and undone if it does not save tokens. If the provider rejects the compressed body, the original is resent. Worst case is zero savings.

Existing prompt-cache prefixes (cache_control) are left alone. First-arrival recoverable shaping (llmtrim recall r_…) is off by default; live-zone tool-output windowing still runs, and a re-run of the same tool ships in full. To skip shaping for a command (verbatim stdout, including a terminal trailer), set toolout_passthrough = ["*gpt.sh*"] or prefix the command with LLMTRIM_TOOL_OUTPUT=passthrough. Lines starting with LLMTRIM_KEEP: survive windowing even when the rest is clipped.

All 10 compressors

Stages run in savings order. Nothing under a cache_control marker is rewritten.

Stage	What it does	When it runs
tool-output	Lossless template fold first, then window logs · diffs · grep · dumps down to errors / changes / matches; opt-in first-arrival recall can restore omitted cache-boundary results with `llmtrim recall`	tool results
cache discipline	Mark + stabilize the invariant prefix (sort tools/schema · OpenAI `prompt_cache_key`) so it stays cached	tools
lexical retrieval	BM25+ ranking with RM3 feedback · TextTiling topic cuts · budgeted non-redundant selection; question protected	long context
skeletonization	tree-sitter keeps relevant function bodies, drops the rest to signatures (14 languages)	code
serialize + hygiene	Minify JSON, encode record arrays to TOON or CSV, Unicode-normalize	always · lossless
json sample	Down-sample huge record arrays: first/last + outliers + a query-biased diverse sample	big JSON
dedup	Collapse duplicate + near-duplicate lines (prose only)	always
output control	Terse instruction · Chain-of-Draft · token budget · native JSON schema · anti-overthink directive (quantized reasoning) · agent-loop frugality directive	auto
tool layer	Static tool selection + description trimming	tools
multimodal	Downscale images to the provider's resolution cap	images

Default auto enables each stage only where it pays. safe is lossless-only. Config →

Claude Code

When ~/.claude exists, setup, update, and ensure wire /sub (and routed subagents). No separate install command for that.

Feature	What you get
`/sub`	Per-window: `/sub on [optional:codex\	kimi\	grok] `·` /sub off `·` /sub status`

Subscription reroute (sub) (opt-in; may conflict with provider ToS)

Send Claude Code through CLIProxyAPI instead of Anthropic, or as a fallback hop when the current path fails. Login is CLIProxyAPI's TUI; decide for yourself whether that fits the provider ToS.

llmtrim sub on                  # install + start CLIProxyAPI, enable redirect

llmtrim sub on grok # write opus/sonnet/haiku/fable → that CLI's models llmtrim sub auth # CLIProxyAPI TUI — sign in llmtrim sub models # official + live sidecar models llmtrim sub map on opu

Only the beginning is shown; read the rest in the repo

llmtrim

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Search & AI memoryi

Common questions

Alternatives

Context7

Codebase Memory

Exa

README from the maker

What it does

Get started

open a new terminal, then keep working

Windows (PowerShell)

Package managers

Day to day

In action

after (errors verbatim; INFO folded losslessly)

Claude Code