Open-source developer alpha
Get Kin running in a few minutes.
Install the CLI, let Kin configure supported clients, initialize one repository, and ask the graph your first question.
Inspect release detailsAgent setup prompt
Paste this into Claude, ChatGPT, Cursor, Gemini, or Antigravity and the agent installs Kin and registers its own MCP client.
Works with agent CLIs and IDEs that support MCP. macOS, Linux, and Windows, where native support is early and WSL2 is recommended.
Show the promptHide the prompt
Please set up Kin for this project. Kin is the system of record for AI-written software.
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 the CLI.
curl -fsSL https://get.kinlab.dev/install | KIN_NO_SETUP=1 sh
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.
Now wire the agents. This detects the AI clients Kin supports and registers its MCP
server with each one it finds.
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.First useful result
Five steps from install to context.
Run the graph on a repository you know. Kin reports its actual state before you depend on the answer.
- 01
Install the Kin CLI
Use the checksum-verified public installer for macOS or Linux. Windows has a separate PowerShell path.
curl -fsSL https://get.kinlab.dev/install | sh - 02
Connect supported tools
Run setup once. Kin reports which detected clients it configured and which still need attention.
kin setup - 03
Initialize and verify
Build the repository graph, then confirm its actual readiness before querying it.
cd /path/to/repository kin init kin status - 04
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 - 05
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"
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.
Connect an agent
One MCP entry, every client.
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.
Kin's MCP server speaks stdio and runs on your machine. Run kin init in a repository first, then point a client at it, so its first tool call has a graph to answer from.
Both links hand the client the same entry the blocks below spell out.
Working in VS Code? The Kin extension puts the entity explorer, semantic search and trace beside the code you are reading. It is listed on the VS Code Marketplace and on Open VSX.
Claude Code
~/.claude.json
{ "mcpServers": { "kin": { "command": "npx", "args": [ "-y", "@kinlab/kin", "mcp", "start" ] } } }Cursor
~/.cursor/mcp.json
{ "mcpServers": { "kin": { "command": "npx", "args": [ "-y", "@kinlab/kin", "mcp", "start" ] } } }Codex CLI
~/.codex/config.toml
[mcp_servers.kin] command = "npx" args = ["-y", "@kinlab/kin", "mcp", "start"]Gemini CLI
~/.gemini/settings.json
{ "mcpServers": { "kin": { "command": "npx", "args": [ "-y", "@kinlab/kin", "mcp", "start" ] } } }Antigravity
~/.gemini/config/mcp_config.json, or .agents/mcp_config.json in a workspace
{ "mcpServers": { "kin": { "command": "npx", "args": [ "-y", "@kinlab/kin", "mcp", "start" ] } } }Windsurf
~/.codeium/windsurf/mcp_config.json
{ "mcpServers": { "kin": { "command": "npx", "args": [ "-y", "@kinlab/kin", "mcp", "start" ] } } }JetBrains AI Assistant
Settings > Tools > AI Assistant > Model Context Protocol (MCP), as raw JSON over STDIO
{ "mcpServers": { "kin": { "command": "npx", "args": [ "-y", "@kinlab/kin", "mcp", "start" ] } } }Warp
~/.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
opencode.json
{ "mcp": { "kin": { "type": "local", "command": [ "npx", "-y", "@kinlab/kin", "mcp", "start" ] } } }Amp
One command, or the amp.mcpServers setting
amp mcp add kin -- npx -y @kinlab/kin mcp start
Platform notes
Core support and filesystem projection are separate.
You do not need filesystem projection to use the CLI, daemon, or MCP tools.
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.
npm
npm i -g @kinlab/kinWindows PowerShell
irm https://get.kinlab.dev/install.ps1 | iexYour next step is a useful query.
After setup, ask Kin where a behavior lives or what depends on a change.
Kin is an early alpha. If you want a note when something real changes, leave an email.
No newsletter, no drip, just the changes worth knowing about.
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