kg
概要
作者による説明(英語の原文)
Local knowledge graph memory MCP server for AI assistants.
PopNx MCPの3つの柱
この3つを1つのスコアにまとめることはしません。意見、測定、人気はそれぞれ別のものだからです。
- 利用者の声iレビューはまだありませんレビューは今後のステップで、アカウント機能と一緒に始まります
- 私たちの基本チェックi1項目中1項目に合格公開情報からチェック。コードはまだスキャンしていません
- 利用状況iとても新しい★ –
できること
このサーバーのやさしい解説はまだ書いていません。以下はレジストリから読み取った事実です。
- 接続方法: –
- トランスポート:
- カテゴリ: 検索・AIの記憶
インストール
使っているAIアプリを選んで、設定をコピーしてください。
インストールの前に
- 基本チェック:1項目中1項目に合格
- あなたのパソコンで、あなたのユーザーと同じ権限(ファイル、ネットワーク、アプリ)で動きます。
- レジストリには、入力が必要なキーは載っていません。
作者はレジストリにインストール情報を載せていません。下にある作者のREADMEをご覧ください。
できること
パソコンで動くサーバーです。インストールすると、ツール一覧がAIアプリに表示されます。
基本チェックi
誰でも見られる情報をチェックしており、コードは読んでいません。当てはまらない項目は数に入れていません。 · 2026年11月8日にチェック
- ✓ 合格
作者とリポジトリが一致
レジストリ名は、リポジトリを所有するGitHubアカウント nnar1o で確認済みです
- … 未チェック
今もメンテナンスされている
このリポジトリはまだGitHubから読み取っていません
- … 未チェック
ライセンスがある
このリポジトリはまだGitHubから読み取っていません
- – 対象外
エンドポイントが応答する
これはパソコンで動くため、インターネット上のエンドポイントはありません
- – 対象外
OAuthでサインイン
パソコンで動き、代わりにローカルの設定を使います
- … 未チェック
ツールの説明に隠れた指示がない
パソコンで動くサーバーです。ツール一覧を読み取れるのは、インターネット上のエンドポイントがある場合だけです
検索・AIの記憶のほかのサーバーとの比較i
- このカテゴリのほかの5688件のうち0%よりスターが多い
- このカテゴリのほかの5688件のうち28%より最近レジストリに公開
- 基本チェックの結果が、このカテゴリのほかの5688件のうち100%と同じかそれ以上
レジストリでこのカテゴリにある有効なすべてのサーバーと比較しています。スターは、公開者がリポジトリの所有者である場合だけ数えます。
よくある質問
kgは安全ですか?
当てはまる基本チェック1項目のうち1項目に合格しました。コードは読んでいません。キーやトークンを追加する前に、サーバーがどんなアクセスを求めているかを確認し、必要な権限だけを持つキーを使ってください。
どんなキーやコードが必要ですか?
レジストリには必須のキーは載っていません。ただし、最初に使うときにサインインを求めるサーバーもあります。
パソコンで動きますか、それともクラウドですか?
あなたのパソコンで動きます。必要なときにAIアプリが起動し、データは作者のサーバーを通りません(サーバー自体がオンラインサービスを呼び出す場合を除きます)。
今もメンテナンスされていますか?
GitHubリポジトリの最終更新は–、最新バージョン(0.3.0)がレジストリに届いたのは3 か月前です。
どのアプリで使えますか?
Claude Desktop、Claude Code、Cursor、VS CodeなどのMCP対応アプリで使えます。
代わりに使えるものは?
同じカテゴリには:Context7, Codebase Memory, Exa
代わりの候補
同じカテゴリで、ほかの作者が作ったサーバー。スターの多い順。
カードの見方:↓ 週あたりのダウンロードi★ GitHubスターi3つの柱:レビュー · チェック · 利用状況i更新=最新バージョンをレジストリに公開した日i
Codebase Memory
Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.
Exa
Connect AI agents to Exa for web search, content fetching, and multi-step research.
作者のREADME
READMEを開く
作者のリポジトリから書かれたとおりにコピーしたものです。PopNx MCPは内容を確認・推奨していません。HTML、画像、スクリプトは取り除いています。リンクはこのサイトの外へ移動します。
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
*.kgfiles 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:
- restart your AI client,
- confirm the
kgMCP server is available, - 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
| Verb | What 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 value | update 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 |
stats | show 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 | |
strict | disable 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
useto 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
paymentsgraph?" - "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.kgtext graphs.backend = "redb"stores graphs in.dbfiles.graph_dirsets a primary graph directory.graph_dirsadds extra directories scanned bykg listand 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
*.kggraph 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:
*.kgis the main graph file you usually want to review and commit,*.kglogis a local access/feedback log,*.kgindexis a generated local index,*.event.logis a local append-only change timeline,*.bakis the previous on-disk version from the last write,*.bck.*.gzare periodic compressed backup snapshots,*.migration.logis 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 referencedocs/ai-prompt-graph-from-docs.md- longer prompt for document ingestiondocs/build-graph-from-docs.md- graph-building workflow from docsdocs/troubleshooting.md- common issues
Contact
For questions or feedback: nnar10@proton.me