
kg
io.github.nnar1oPublished by GitHub account nnar1ov0.3.0Updated Oct 27, 2026
Local knowledge graph memory MCP server for AI assistants.
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.
Installation
In SourceWeft
- Open kg in the dashboard and add it to a workspace.
- 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]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
From script
Recommended install:
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:
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.
Core verbs
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:
Example prompt with documents:
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:
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:
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:
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:
Tips
Project config (.kg.toml)
kg looks for .kg.toml in the current directory and its parent directories.
Example:
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:
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:
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: [email protected]
Source: README.md at commit 59f2de8
Tools
0Version history
1- v0.3.0LatestSep 16, 2026
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- Tools listed15 pts
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