kg

io.github.nnar1oPublished by GitHub account nnar1ov0.3.0Updated Oct 27, 2026

Local knowledge graph memory MCP server for AI assistants.

VerifiedSTDIODesktop onlyKnowledge & MemoryDeveloper Tools

Overview

AI-generated overview

Local knowledge graph memory server that gives an AI assistant persistent, editable project memory stored as graph files on your machine.

What you can do
  • Search, fetch and list graph nodes, edges, types, relations and graphs, plus graph statistics
  • Create, modify and delete nodes with typed IDs and named fields
  • Connect and disconnect typed relations between nodes
  • Switch the active graph and run short verb-first commands, including feedback on search results
  • Build or extend a graph from project documentation, and index a directory tree into a separate generated structure with code symbols and document sections
When to use it
Useful when you want an assistant to carry structured project knowledge across sessions — architecture, decisions, incidents, rules, dependencies and workflows — instead of re-reading documents each time, and when you want that memory inspectable, diffable and versionable in Git.
Requirements
Runs as a local stdio MCP server on the user's machine; the README installs the kg-cli binary from crates.io, an install script or GitHub Releases and points the client at the kg-mcp executable path. Graphs default to a local directory and can be configured through a .kg.toml project file. No accounts, API keys or environment variables are declared.
Before you install
The project is labeled beta, so APIs may change and bugs are expected. The assistant can create, modify and delete nodes and edges in your graphs, so review changes before committing. Graphs and sidecar logs, indexes and backups are written to local directories; keep only the main graph files in Git and ignore the rest.

Installation

In SourceWeft

  1. Open kg in the dashboard and add it to a workspace.
  2. Enable the server for the chats that should use its tools.

Desktop only via STDIO. STDIO servers start a local process, so they need the SourceWeft desktop host.

Other MCP clients

Follow the launch instructions in the repository.

README

kg - local knowledge graph for your AI assistants

[image]

[CI] [Release] [License]

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

sh
cargo install kg-cli

From script

Recommended install:

sh
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:

json
{  "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.

text
find "compressor defrost"get concept:refrigeratoradd concept:smart_fridge --name "Smart Fridge" --description "Connected refrigerator"modify concept:smart_fridge --importance 0.9remove concept:old_ideaconnect process:compressor_control TRIGGERS process:auto_defrostdisconnect process:compressor_control TRIGGERS process:auto_defrostlist nodesstatsuse fridgehelp

Core verbs

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

IDs

Format: <type>: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 <graph> to switch graphs within a script.
  • Canonical kg <graph> 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:

text
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:

text
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:

sh
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:

text
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:

text
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:

text
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:

toml
backend = "json" # json backend writes native .kg files by defaultgraph_dir = ".kg/graphs"graph_dirs = ["../shared-graphs", "../team-graphs"]nudge = 20user_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:

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:

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

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

Documentation

Contact

For questions or feedback: [email protected]

Source: README.md at commit 59f2de8

Tools

0
SourceWeft has not started this package in its sandbox yet, so its tools are not known.

Version history

1
  1. v0.3.0LatestSep 16, 2026

Score

50% of the applicable checks met

How complete this listing is, computed from the registry entry, the endpoint and public sources. Not a rating of the server and not a statement that it is safe or working today: reachability is a dated check, and no installs or reviews go into the score.

  • Reached in the last 30 days25 pts

    Keep the endpoint answering the MCP handshake; SourceWeft probes it every 30 days.

  • Auth declared10 pts

    Declare the credential headers in the registry entry, or keep the endpoint answering the probe so its auth can be classified.

  • Tools listed15 pts

    Answer tools/list without a credential, or ship a package that starts in a sandbox without one.

  • DescriptionRequired15 pts
  • Logo10 pts
  • Documentation15 pts
  • License10 pts