Public alpha, open source
Try Kin on your computer.
Choose your setup below, then try Kin on a small code project you know. Ask what connects to a part of the code and compare the answer with the source.
Start with a disposable copy. Language and workflow support are limited; keep your tests and review.
Inspect release detailsClaude Code
Skip the manual steps. Add the plugin.
These commands connect Claude Code to Kin and add its review skills. Kin's command-line tool downloads automatically when first needed.
/plugin marketplace add firelock-ai/kin
/plugin install kin@kinCodex plugin: plugins/kin-codex in the kin repo.
Cursor plugin: plugins/kin-cursor in the kin repo.
First useful result
Set up one project. Ask one question.
Use a small code project you know. Check what Kin could read before depending on its answers.
- 01
Install Kin and connect your tools
One command, the same on macOS, Linux, Windows and WSL. It installs Kin, registers it with the AI coding tools it finds, and adds kin to your shell profile. Needs Node.js 20 or newer. Open a new terminal when it finishes.
npx -y @kinlab/kin setup - 02
Build the graph for one repository
Kin reads the repository and its history once, then reports what it holds. Use a small project you know, so you can check the answers.
cd /path/to/repository kin init kin status - 03
Enable reference edges
Kin resolves references across files through a language server for each language. This installs the ones your host is missing, and reports any that need a toolchain Kin will not install for you.
kin doctor --fix --install-language-servers - 04
Ask your first question
Start with a task you know. Compare the answer with the repository and keep any reported gaps visible.
kin locate "where are webhook retries handled"
Optional: vector similarity setupVector similarity setup
Vector similarity is optional. Run kin embed when you want it, and budget for the model download and the local memory it needs. On a small repository, a fast kin init can outrun the 523 MB embedding model download in the background, so the first kin locate may run without semantic ranking; run it again after the download finishes.
Platform notes
Check support for your computer.
macOS and Linux support the core tools. Native Windows support is early and x64 only; use WSL2 on Arm.
See platform notesHide platform notes
Core supported. Filesystem projection may be limited by SIP or hardened executables.
Core supported. Filesystem projection requires a compatible glibc host.
Native Windows support is early and x64 only. Use WSL2 on Arm.
Every platform
npx -y @kinlab/kin setupThe same command on all three. Needs Node.js 20 or newer.
macOS and Linux, no Node
curl -fsSL https://get.kinlab.dev/install | shWindows PowerShell
irm https://get.kinlab.dev/install.ps1 | iexnpm global
npm i -g @kinlab/kinConnect an agent
Connect your AI coding tool.
Agent setup prompt
Paste this into a supported AI coding tool with local terminal access. It leads with the same command as the top of this page, in the form that answers the wizard's questions for an agent, then initializes the repository and checks the result.
Show the promptHide the prompt
Please set up Kin for this project. Kin records named code, resolved relationships and changes as repository data, with familiar files available to supported tools.
It lets you ask the graph where code lives instead of searching files for it.
Work from the root of the repository we are setting up.
First install Kin and connect the AI clients on this machine. One command does both, and it
is the same on macOS, Linux, Windows and WSL. It needs Node 20 or newer.
npx -y @kinlab/kin setup --intent agent --no-interactive
If this machine has no Node, use the installer instead. It puts the same binaries in the same
place.
curl -fsSL https://get.kinlab.dev/install | KIN_NO_SETUP=1 sh
Either way Kin lands in ~/.kin/bin and adds that directory to your shell profile, so start a
new login shell before you use the bare name.
exec "$SHELL" -l
Then admit this repository into the graph. Kin reads the whole reachable Git history, so a
large repository can take a while. This cost is paid once.
kin init
After init the daemon keeps preparing in the background, so the first agent calls on a large
repository can take noticeably longer to answer.
Some clients record the repository they answer for, so run setup once more from inside the
initialized repository to pick those up.
kin setup --intent agent
Confirm it worked before going further.
kin setup doctor
Check that the MCP clients line reports ok.
Now install the language servers. Kin resolves references across files through a language
server for each language in the repository, and a fresh host usually has none, so do this
before you rely on find_references.
kin doctor --fix --install-language-servers
Kin uses a toolchain you already have when there is one, and otherwise downloads a release
binary it checks against a digest it ships with. It prints what it installed and where it came
from, and it tells you when the server it installed still needs something Kin will not install
for you. Rust is the case worth knowing: rust-analyzer reads a project by running cargo, so on
a machine with no Rust toolchain it starts and resolves nothing, and Kin says so. If it reports
that it could not finish, keep going. Everything else works, and Kin reports reference edges as
pending for that language rather than telling you a symbol has no callers.
Then restart your AI client completely, because clients read their MCP configuration at
startup and will not see Kin until they do.
After the restart you should see Kin tools available, including semantic_locate,
get_context_pack, and find_references. find_references answers for a language only once its
language server is installed, which is what the step above is for. A good first request looks
like this.
Use Kin to explore this codebase. Run semantic_locate to find the main entry point, then
get_context_pack on that file.
From here, prefer Kin's semantic tools over grep and raw file reads when you are exploring
this repository. Use semantic_locate to find symbols by meaning, get_context_pack to pull a
structured bundle around a file or symbol, and find_references to see what depends on an
entity. These read the graph directly and return what the repository actually recorded rather
than what a text match happened to hit.
One note on platforms. This works on macOS, on Linux, and on Windows. Native Windows
support is early and x64 only; use WSL2 on Arm.How automatic and manual client setup differ
kin setup writes this server into the clients it detects, so most people never touch a JSON file. It writes an absolute path to the Kin it installed; the blocks below use the npm launcher instead, which needs no path. Kin's own health check accepts either and grades any other argument vector misconfigured, so use these for the clients setup does not cover yet.
The command at the top of this page does both steps for most tools: it installs Kin and registers it with the AI coding tools on your machine. Then run kin init in the repository you want Kin to answer for. A few tools record which repository they serve, so if yours is one of them, run kin setup --intent agent once more from inside that repository and Kin will pick it up.
The Kin VS Code extension lets you explore code and its connections in the editor. Find it on the VS Code Marketplace and Open VSX.
Set up an AI tool manually
Choose your client to see its configuration location and exact settings. These connect to Kin on your machine.
~/.claude.json
{
"mcpServers": {
"kin": {
"command": "npx",
"args": [
"-y",
"@kinlab/kin",
"mcp",
"start"
]
}
}
}~/.cursor/mcp.json
{
"mcpServers": {
"kin": {
"command": "npx",
"args": [
"-y",
"@kinlab/kin",
"mcp",
"start"
]
}
}
}~/.codex/config.toml
[mcp_servers.kin]
command = "npx"
args = ["-y", "@kinlab/kin", "mcp", "start"]~/.gemini/settings.json
{
"mcpServers": {
"kin": {
"command": "npx",
"args": [
"-y",
"@kinlab/kin",
"mcp",
"start"
]
}
}
}~/.gemini/config/mcp_config.json, or .agents/mcp_config.json in a workspace
{
"mcpServers": {
"kin": {
"command": "npx",
"args": [
"-y",
"@kinlab/kin",
"mcp",
"start"
]
}
}
}~/.codeium/windsurf/mcp_config.json
{
"mcpServers": {
"kin": {
"command": "npx",
"args": [
"-y",
"@kinlab/kin",
"mcp",
"start"
]
}
}
}Settings > Tools > AI Assistant > Model Context Protocol (MCP), as raw JSON over STDIO
{
"mcpServers": {
"kin": {
"command": "npx",
"args": [
"-y",
"@kinlab/kin",
"mcp",
"start"
]
}
}
}~/.warp/.mcp.json, or .warp/.mcp.json in a project. The settings UI is at warp://settings/mcp
{
"mcpServers": {
"kin": {
"command": "npx",
"args": [
"-y",
"@kinlab/kin",
"mcp",
"start"
]
}
}
}opencode.json
{
"mcp": {
"kin": {
"type": "local",
"command": [
"npx",
"-y",
"@kinlab/kin",
"mcp",
"start"
]
}
}
}One command, or the amp.mcpServers setting
amp mcp add kin -- npx -y @kinlab/kin mcp startAsk a question you can check.
After setup, ask Kin where a behavior lives or what depends on a change.
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