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kg

제작자: nnar1o버전 0.3.0i★ –i–
레지스트리 이름io.github.nnar1o/kg-mcp

설치

개요

제작자가 쓴 설명(영어 원문)

Local knowledge graph memory MCP server for AI assistants.

PopNx MCP의 세 기둥

이 세 가지는 절대 하나의 점수로 합치지 않아요. 의견, 측정, 인기는 서로 다른 것이니까요.

할 수 있는 일

이 서버는 아직 쉬운 말로 된 요약을 쓰지 못했어요. 아래는 레지스트리에서 읽어 온 사실이에요.

  • 연결 방식: –
  • 전송 방식:
  • 카테고리: 검색 및 AI 기억

설치

쓰고 있는 AI 앱을 고른 다음 설정을 복사하세요.

설치하기 전에

  • 기본 점검: 1개 중 1개 통과
  • 내 컴퓨터에서 내 사용자 계정의 권한(파일, 네트워크, 앱)으로 실행돼요.
  • 레지스트리에 입력해야 할 키가 나와 있지 않아요.

제작자가 레지스트리에 설치 정보를 올리지 않았어요. 아래 제작자의 README를 확인하세요.

할 수 있는 일

내 컴퓨터에서 실행돼요. 설치하면 AI 앱에 도구 목록이 나타나요.

기본 점검i

누구나 볼 수 있는 정보를 점검했고, 코드는 읽지 않았어요. 해당하지 않는 항목은 개수에서 뺐어요. · 2026년 11월 8일 점검

  • ✓ 통과

    제작자와 저장소 일치

    레지스트리 이름이 GitHub 계정 nnar1o 기준으로 확인되었고, 이 계정이 저장소를 소유하고 있어요

  • … 아직 점검 안 함

    계속 관리되고 있음

    아직 GitHub에서 이 저장소를 읽어 오지 않았어요

  • … 아직 점검 안 함

    라이선스 있음

    아직 GitHub에서 이 저장소를 읽어 오지 않았어요

  • – 해당 없음

    엔드포인트 응답

    내 컴퓨터에서 실행되는 서버라 인터넷 엔드포인트가 없어요

  • – 해당 없음

    OAuth로 로그인

    내 컴퓨터에서 실행되며, 대신 로컬 설정을 사용해요

  • … 아직 점검 안 함

    도구 설명에 숨겨진 지시 없음

    내 컴퓨터에서 실행돼요. 도구 목록은 인터넷 엔드포인트가 있는 서버에서만 읽을 수 있어요

검색 및 AI 기억 카테고리의 다른 서버와 비교i

  • 이 카테고리의 다른 서버 5676개 중 0%보다 스타가 많아요
  • 이 카테고리의 다른 서버 5676개 중 29%보다 최근에 레지스트리에 공개됐어요
  • 기본 점검 결과가 이 카테고리의 다른 서버 5676개 중 100%와 같거나 더 좋아요

레지스트리에서 이 카테고리의 활성 서버 전체와 비교했어요. 스타는 게시자가 저장소를 소유한 경우에만 세요.

자주 묻는 질문

kg, 안전한가요?

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.

어떤 키나 코드가 필요한가요?

레지스트리에 필수 키가 나와 있지 않아요. 다만 처음 쓸 때 로그인을 요구하는 서버도 있어요.

내 컴퓨터에서 실행되나요, 클라우드에서 실행되나요?

내 컴퓨터에서 실행돼요. AI 앱이 필요할 때 실행하고, 데이터는 제작자의 서버를 거치지 않아요(서버 자체가 온라인 서비스를 호출하는 경우는 제외).

아직 관리되고 있나요?

GitHub 저장소는 마지막으로 – 변경됐고, 최신 버전(0.3.0)은 3개월 전 레지스트리에 올라왔어요.

어떤 앱에서 쓸 수 있나요?

Claude Desktop, Claude Code, Cursor, VS Code처럼 MCP를 지원하는 앱에서 쓸 수 있어요.

대신 쓸 수 있는 건 뭐가 있나요?

같은 카테고리에는: Context7, Codebase Memory, Exa

대안

같은 카테고리에서 다른 제작자가 만든 서버예요. 스타 순으로 정렬했어요.

카드 보는 법:★ GitHub 스타i세 기둥: 리뷰 · 점검 · 사용 정도i업데이트 = 최신 버전을 레지스트리에 공개한 날i

Context7

제작자: upstash

Up-to-date docs for AI agents

컴퓨터 또는 클라우드★ 64,224MIT어제 업데이트
설치

Codebase Memory

제작자: DeusData

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

내 컴퓨터에서 실행★ 48,148MIT21일 전 업데이트
설치

Exa

제작자: exa-labs

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

클라우드에서 실행★ 5,213MIT2개월 전 업데이트
설치

제작자의 README

README 열기

제작자의 저장소에서 쓰인 그대로 가져왔어요. PopNx MCP가 검토하거나 보증한 내용이 아니에요. HTML, 이미지, 스크립트는 제거했어요. 링크를 누르면 이 사이트를 벗어나요.

kg - local knowledge graph for your AI assistants

Beta - APIs may still change and some bugs are still expected.

kg gives your AI assistant persistent, structured, editable project memory stored locally as a knowledge graph.

Instead of relying only on document chunk retrieval, you can keep architecture, decisions, incidents, rules, dependencies, and workflows in a graph that is readable, reviewable, and Git-friendly.

Use it when you want your assistant to understand an existing project across sessions — not start from zero every time.

Why use it

  • Persistent memory — keep project knowledge between conversations
  • Structured, not fuzzy — inspect nodes, edges, facts, and gaps directly
  • Editable and reviewable — store graphs as *.kg files with readable diffs
  • Local-first — your project memory stays on your machine in git-friendly format
  • Works with MCP clients — connect it as a local stdio MCP server

Why not just RAG

Classic RAG is good for retrieving text chunks from documents.

kg-mcp is better when you want:

  • stable project memory instead of repeated retrieval
  • explicit facts, relations, and dependencies
  • graph updates during real work with the assistant
  • something you can inspect, version, diff, and improve over time

Installation

From crates.io
cargo install kg-cli
From script

Recommended install:

curl -sSL https://raw.githubusercontent.com/nnar1o/kg/master/install.sh | sh

You can also download a ready binary from GitHub Releases.

Connect kg-mcp to Your AI Client

Add kg-mcp as a local stdio MCP server.

Example config:

{

"mcpServers": { "kg": { "command": "/absolute/path/to/kg-mcp" } } }

After that:

  1. restart your AI client,
  2. confirm the kg MCP server is available,
  3. start using the prompts below.

Full MCP setup and reference: docs/mcp.md

SCL quickstart

kg understands short, verb-first English commands (SCL — Simple Command Language). The active graph is resolved from your config automatically.

find "compressor defrost"

get concept:refrigerator add concept:smart_fridge --name "Smart Fridge" --description "Connected refrigerator" modify concept:smart_fridge --importance 0.9 remove concept:old_idea connect process:compressor_control TRIGGERS process:auto_defrost disconnect process:compressor_control TRIGGERS process:auto_defrost list nodes stats use fridge help

Core verbs
VerbWhat it does
find search nodes by text
get fetch one node by id
add --name "Name"create a node (type inferred from id prefix)
modify --field valueupdate node fields
remove delete a node
connect create an edge (alias: add edge)
disconnect delete an edge (alias: remove edge)
`list nodes\edges\types\relations\graphs`list graph contents
statsshow graph statistics
use switch active graph
help [verb]get help for a verb or all
`feedback yes\no\nil\pick `give feedback on search results
strictdisable defaults for following lines
IDs

Format: :snake_case — e.g. concept:fridge, bug:door_seal, process:compressor_cycle.

Relations

HAS USES STORED_IN TRIGGERS CREATED_BY AFFECTED_BY AVAILABLE_IN DOCUMENTED_IN DEPENDS_ON TRANSITIONS DECIDED_BY GOVERNED_BY READS_FROM

Tips
  • Flags go after positional args. Quote multiword values.
  • Separate commands with ; or newlines. Lines starting with # are comments.
  • Use use to switch graphs within a script.
  • Canonical kg node find ... commands still work as fallback.
  • Full SCL reference: docs/scl.md

Generate a Graph

This is the first workflow for a new project: ask the assistant to create or extend a graph from your documentation.

By default, graphs are stored in ~/.kg/graphs as *.kg files.

Minimal prompt:

You are connected to kg-mcp.

Project graph name: payments

Build or extend this graph from the project documentation I provide. Use payments as the graph name for all graph operations.

Only add facts grounded in source material. If an important fact is missing and can be inferred safely from the provided docs, update the graph. If something is ambiguous, ask or record it as a note instead of inventing facts.

Example prompt with documents:

Use kg-mcp to build or extend the `payments` graph from these documents:
  • docs/payments/overview.md
  • docs/payments/retries.md
  • docs/payments/providers.md

Only add facts grounded in the documents. If something is ambiguous, keep it out of the graph or record it as a note. When you finish, summarize what was added, what remains unclear, and what document should be ingested next.

Longer prompt for this workflow: docs/ai-prompt-graph-from-docs.md

For a ready-made repository example, run cargo run --bin repo-example to generate repo-example.kg from this repo.

Automatic graph for a directory

kg can turn an existing folder into a graph automatically. It scans the directory tree, recognizes many common file types, extracts symbols for Rust, Java, JavaScript/TypeScript, Python, and C/C++, and keeps the generated structure separate from the manual graph.

For markdown-like documents, it also creates document (GDOC) and chapter (GSEC) nodes with section content.

It is a fast way to get a useful map of a codebase or workspace without modeling everything by hand. The generated index is local, refreshable, and safe to ignore in git.

Example:

cargo run --bin repo-example

This generates repo-example.kg from this repository as a local demo.

Ask the Assistant About Facts in the Graph

Once the graph exists, the normal workflow is to ask the assistant to inspect it and answer questions from it.

Example prompt:

Use kg-mcp to inspect my existing `payments` graph.

I want to understand:

  • how payment authorization works,
  • what triggers retries,
  • which external providers are involved,
  • which datastore reads and writes are part of the flow.

If the graph is missing critical information, say exactly what is missing.

Other useful questions:

  • "What rules control retries in the payments graph?"
  • "Which systems write to the orders datastore?"
  • "What is missing or weak in this graph?"
  • "Which nodes and edges explain the authorization flow?"

Add or Update Facts Through the Assistant

You can also ask the assistant to improve the graph while you work.

Example prompt:

Use kg-mcp to review my existing `payments` graph.

Find:

  • missing important nodes,
  • weak descriptions,
  • missing facts,
  • suspicious or low-value edges.

Apply safe improvements where possible. Only add facts grounded in the graph, the provided docs, or the current discussion. If something is ambiguous, leave it out or add a note.

When you finish, summarize:

  • what was wrong,
  • what you changed,
  • what still needs manual review.

This works best when your main system prompt or project prompt already tells the assistant which graph belongs to the project.

Minimal project-level prompt:

You are connected to kg-mcp.

Project graph name: payments. Use this graph for relevant reads and updates in this project. If you notice important missing information that is grounded in the available docs or conversation context, update the graph as part of your work. If uncertain, ask or add a note instead of inventing facts.

Tips

Project config (.kg.toml)

kg looks for .kg.toml in the current directory and its parent directories.

Example:

backend = "json" # json backend writes native .kg files by default

graph_dir = ".kg/graphs" graph_dirs = ["../shared-graphs", "../team-graphs"] nudge = 20 user_short_uid = "dev_01"

[graphs] payments = "graphs/payments.kg"

Notes:

  • backend = "json" is the default and prefers .kg text graphs.
  • backend = "redb" stores graphs in .db files.
  • graph_dir sets a primary graph directory.
  • graph_dirs adds extra directories scanned by kg list and graph resolution.
Keep Graphs in Git

The default graph directory is ~/.kg/graphs.

You can put that directory under git.

Recommended approach:

  • keep the main *.kg graph files in git,
  • ignore generated sidecars and local operational files,
  • treat backup snapshots and event logs as local machine history unless you explicitly want to version them.

Suggested .gitignore:

*.kglog

*.kgindex *.event.log *.migration.log *.bak *.bck.*.gz

In practice:

  • *.kg is the main graph file you usually want to review and commit,
  • *.kglog is a local access/feedback log,
  • *.kgindex is a generated local index,
  • *.event.log is a local append-only change timeline,
  • *.bak is the previous on-disk version from the last write,
  • *.bck.*.gz are periodic compressed backup snapshots,
  • *.migration.log is a migration report when older graphs are converted.

*.kg is git-friendly and intentionally structured to make diffs readable and merges easier when several people work on the same graph.

Export a Graph to HTML

To generate an interactive HTML view of a graph:

kg graph payments export-html --output payments.html

You can keep the generated HTML as a shareable visual snapshot of the current graph.

Documentation

  • docs/mcp.md - MCP setup and tool reference
  • docs/ai-prompt-graph-from-docs.md - longer prompt for document ingestion
  • docs/build-graph-from-docs.md - graph-building workflow from docs
  • docs/troubleshooting.md - common issues

Contact

For questions or feedback: nnar10@proton.me

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