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, wires your shell, and enables recoverable tool-output shaping. When Claude Code is present, it also turns on /sub. You do not run a separate install for that.

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

# 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

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

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:

[!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. On shell-capable agent turns, a newly arriving tool result may be shaped once before its first cache write; the exact raw result remains recoverable with the emitted llmtrim recall r_… command.