Context7
Up-to-date docs for AI agents
제작자가 쓴 설명(영어 원문)
MCP server and proxy that compresses LLM prompts, tool output, and replies to cut token cost.
이 세 가지는 절대 하나의 점수로 합치지 않아요. 의견, 측정, 인기는 서로 다른 것이니까요.
이 서버는 아직 쉬운 말로 된 요약을 쓰지 못했어요. 아래는 레지스트리에서 읽어 온 사실이에요.
쓰고 있는 AI 앱을 고른 다음 설정을 복사하세요.
필요한 것
어떤 앱을 쓰세요?
위에서 앱을 고르세요. 기억해 두었다가 다음부터는 버튼에 “…에 설치”라고 표시해요.
Claude Desktop → Settings → Developer → Edit Config를 열고 claude_desktop_config.json에 이 내용을 붙여 넣으세요
{
"mcpServers": {
"llmtrim": {
"command": "npx",
"args": [
"-y",
"@llmtrim/cli",
"mcp"
]
}
}
}터미널에서 이 명령어를 실행하세요
claude mcp add llmtrim -- npx -y @llmtrim/cli mcp프로젝트의 .cursor/mcp.json 또는 ~/.cursor/mcp.json에 붙여 넣으세요
{
"mcpServers": {
"llmtrim": {
"command": "npx",
"args": [
"-y",
"@llmtrim/cli",
"mcp"
]
}
}
}브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.
프로젝트의 .vscode/mcp.json에 붙여 넣으세요
{
"servers": {
"llmtrim": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"@llmtrim/cli",
"mcp"
]
}
}
}브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.
이 서버는 내 컴퓨터에서 실행되는데, ChatGPT는 인터넷에 있는 서버에만 연결할 수 있어요. 다른 앱을 고르세요.
선택한 앱은 이 기기의 쿠키에만 저장돼요. 계정은 필요 없어요.
이 설정은 제작자의 레지스트리 정보로 만들었어요. README와 다르면 README를 따르세요.
내 컴퓨터에서 실행돼요. 설치하면 AI 앱에 도구 목록이 나타나요.
누구나 볼 수 있는 정보를 점검했고, 코드는 읽지 않았어요. 해당하지 않는 항목은 개수에서 뺐어요. · 2026년 11월 7일 점검
레지스트리 이름이 GitHub 계정 fkiene 기준으로 확인되었고, 이 계정이 저장소를 소유하고 있어요
아직 GitHub에서 이 저장소를 읽어 오지 않았어요
아직 GitHub에서 이 저장소를 읽어 오지 않았어요
내 컴퓨터에서 실행되는 서버라 인터넷 엔드포인트가 없어요
내 컴퓨터에서 실행되며, 대신 로컬 설정을 사용해요
내 컴퓨터에서 실행돼요. 도구 목록은 인터넷 엔드포인트가 있는 서버에서만 읽을 수 있어요
레지스트리에서 이 카테고리의 활성 서버 전체와 비교했어요. 스타는 게시자가 저장소를 소유한 경우에만 세요.
해당하는 기본 점검 1개 중 1개를 통과했어요. 코드는 읽지 않았어요. 키나 토큰을 넣기 전에 서버가 어떤 권한을 요구하는지 확인하고, 꼭 필요한 권한만 있는 키를 쓰세요.
레지스트리에 필수 키가 나와 있지 않아요. 다만 처음 쓸 때 로그인을 요구하는 서버도 있어요.
내 컴퓨터에서 실행돼요. AI 앱이 필요할 때 실행하고, 데이터는 제작자의 서버를 거치지 않아요(서버 자체가 온라인 서비스를 호출하는 경우는 제외).
GitHub 저장소는 마지막으로 – 변경됐고, 최신 버전(0.13.4)은 2개월 전 레지스트리에 올라왔어요.
Claude Desktop, Claude Code, Cursor, VS Code처럼 MCP를 지원하는 앱에서 쓸 수 있어요. 내 컴퓨터에 Node.js도 있어야 해요.
같은 카테고리에는: Context7, Codebase Memory, Exa
같은 카테고리에서 다른 제작자가 만든 서버예요. 스타 순으로 정렬했어요.
카드 보는 법:★ GitHub 스타i세 기둥: 리뷰 · 점검 · 사용 정도i업데이트 = 최신 버전을 레지스트리에 공개한 날i
Up-to-date docs for AI agents
Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.
Connect AI agents to Exa for web search, content fetching, and multi-step research.
제작자의 저장소에서 쓰인 그대로 가져왔어요. PopNx MCP가 검토하거나 보증한 내용이 아니에요. HTML, 이미지, 스크립트는 제거했어요. 링크를 누르면 이 사이트를 벗어나요.
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
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 →
npm install -g @llmtrim/cli@latest && llmtrim setup
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_PROXYworks (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
irm https://raw.githubusercontent.com/fkiene/llmtrim/main/install.ps1 | iex
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:
~/.llmtrim/ (name-constrained; cannot intercept your bank or email)HTTPS_PROXY + CA trustNo 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"
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.
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
[{}] 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 →
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
앞부분만 보여 드려요. 나머지는 저장소에서 읽어 보세요