Kage
Persistent, verified memory for coding agents — so they stop re-explaining your codebase and never act on stale knowledge. Every memory is checked against your actual code; lives in your repo as plain files, shared via git. No account, no DB. Install: npx -y @kage-core/kage-graph-mcp install
Install / Use
claude mcp add kage-core -- npx -y github:kage-core/KageIf the server publishes to npm under a different name, use that package instead — check the repo README.
MCP Server
Model Context Protocol server
Quality Score
Category
Development & EngineeringSupported Platforms
Our assessment of Kage
Kage scores 87/100 on our quality scale, 2090th of 4,616 Development & Engineering skills we index (top 46%).
Its MCP Server is 16 KB long, well organised into 19 sections with 9 code examples: a thorough specification that gives an agent plenty to work with.
It has 42 GitHub stars, so there is little community track record yet; judge it on its content.
Maintenance, license and trust
- The repository was last updated about 2 months ago, so Kage is actively maintained.
- It is released under GPL-3.0, a copyleft license: you can use it, but modified versions you distribute must carry the same license.
- Its trust signals score 97/100, with no cautions. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.
Safety scan
No issues foundOur scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. An AI review of the same text found nothing harmful.
AI review by kimi-k2.7-code on 2026-10-28. Automated pattern scan on 2026-10-28. It catches known dangerous patterns, not every risk — read a skill before letting an agent act on it.
Kage compared with similar skills
All 4 of these similar skills score higher than Kage; compare them before choosing.
| Skill | Score | Stars | Updated | Format |
|---|---|---|---|---|
| Kage (this skill)by kage-core | 87 | 42 | 59d ago | MCP Server |
| Agent-Reachby Panniantong | 100 | 105.0k | 2d ago | CLAUDE.md |
| taste-skillby Leonxlnx | 100 | 103.8k | today | CLAUDE.md |
| headroomby headroomlabs-ai | 100 | 78.1k | today | CLAUDE.md |
| ai-job-searchby ai-job-search-dev | 100 | 66.9k | today | CLAUDE.md |
Frequently asked questions
- How do I install Kage?
- Run
claude mcp add kage-core -- npx -y github:kage-core/Kage. The install tabs above show the steps for each supported agent. - Which AI agents does Kage work with?
- It is written for Claude Code, Claude Desktop, Cursor and OpenAI Codex, as a MCP Server file. Other agents that read the same format can often use it too.
- Is Kage safe to use?
- Our scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. An AI review of the same text found nothing harmful. It is GPL-3.0-licensed and scores 97/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
- Is Kage still maintained?
- The repository was last updated about 2 months ago, so Kage is actively maintained.
Skill content
View source on GitHubKage manages your memory and agents
State an intent. Kage's orchestrator briefs a coding agent from your repo's own memory, runs it in an isolated git worktree — a single run or a multi-wave goal — and re-runs the checks itself rather than trusting the agent's report:
┌ VERIFIED 3/3 — checks run by Kage, not the agent · build-a-stale-memory-triage-surface-do-n-260818-ec2c
│ "the stale-memory triage surface is built and wired into the review flow"
│ ✓ tests ran npm test --prefix mcp → exit 0 evidence/tests.log
│ ✓ diff-size inspected at most 800 changed lines evidence/diff-size.log
│ ✓ citations inspected every formally cited path exists (directly, or as a unique suffix) in the worktree evidence/citations.log
│ · touched 4 file(s), 212 line(s)
└────────────────────────────────────────────────────────────────
<sub>A real receipt from this repo's own run history. Every row is a command Kage ran or a fact
it inspected — never a claim the agent made about itself. kage merge only lands the code once
the claim holds, and ratifies what the agent learned, so the next brief, yours or a teammate's,
starts smarter.</sub>
That memory is the decisions behind your codebase, the runbook for a tricky deploy, the root cause of a gnarly bug — captured as your agents work and checked against the actual code, so what gets reused stays true. It's kept as plain Markdown files in your repo, conformant to the Google Open Knowledge Format (OKF) so there's no lock-in, and shared with your whole team through git. No account, no database, no API key.
npx -y @kage-core/kage-graph-mcp install
<p>
<a href="https://www.npmjs.com/package/@kage-core/kage-graph-mcp"><img src="https://img.shields.io/npm/v/@kage-core/kage-graph-mcp?color=41ff8f&label=npm" alt="npm version"></a>
<a href="https://www.npmjs.com/package/@kage-core/kage-graph-mcp"><img src="https://img.shields.io/npm/dm/@kage-core/kage-graph-mcp?color=41ff8f" alt="downloads"></a>
<img src="https://img.shields.io/npm/l/@kage-core/kage-graph-mcp?color=41ff8f" alt="license">
<img src="https://img.shields.io/badge/retrieval-0%20deps-41ff8f" alt="zero-dependency retrieval">
<img src="https://img.shields.io/badge/account-not%20required-41ff8f" alt="no account">
<a href="https://github.com/GoogleCloudPlatform/knowledge-catalog/tree/main/okf"><img src="https://img.shields.io/badge/built%20on-Open%20Knowledge%20Format-41ff8f" alt="Built on Google Open Knowledge Format"></a>
</p>
<img src="docs/kage-stats.svg" alt="Kage in numbers: 98.7% R@10 recall, 0% stale served, 18% faster than grep, zero-dependency retrieval, 360+ tests passing, 15 agents supported" width="820">
<p>
<a href="https://kage-core.com/">Website</a> ·
<a href="https://kage-core.com/guide.html">Docs</a> ·
<a href="https://kage-core.com/viewer/">Live viewer</a> ·
<a href="https://www.npmjs.com/package/@kage-core/kage-graph-mcp">npm</a> ·
<a href="https://kage-core.com/demo.html"><b>Book a demo</b></a>
</p>
Works with Claude Code · Codex · Cursor · Windsurf · Gemini CLI · Cline · Goose · Roo Code · Kilo Code · OpenCode · Aider · Claude Desktop · Copilot · OpenClaw · Hermes · any MCP client
🌐 English · 简体中文 · 日本語 · 한국어 · Español · Português (Brasil) · Français · Deutsch · हिन्दी
</div>Install
One command, inside your repo, then restart your agent. That's the whole setup.
npx -y @kage-core/kage-graph-mcp install
It creates .agent_memory/, builds the code graph, writes the AGENTS.md / CLAUDE.md
policy that tells agents to use Kage, auto-detects and wires your agents, and configures
.gitignore + the packet merge driver. Requires Node.js 18+. No account, no API key.
Or just ask your agent to set it up. Paste this into Claude Code, Cursor, or any coding agent:
<details><summary>Other ways (plugin · per-agent · memory-only)</summary>Set up Kage (verified memory for coding agents, https://github.com/kage-core/Kage) in this repo: run
npx -y @kage-core/kage-graph-mcp install, then tell me to restart you.
# Claude Code / Codex plugin
/plugin marketplace add kage-core/Kage # then: /plugin install kage@kage
# wire a single agent (run `kage setup list` for all supported)
kage setup claude-code --project . --write
# memory store only, no agent wiring
kage init --project .
# confirm the harness is live
kage setup verify-agent --agent claude-code --project .
</details>
Delegate work (the orchestrator)
kage room --project . # talk to Kage; it briefs and hires agents for you
kage dispatch "<intent>" --agent claude # one delegated run, briefed from repo memory
kage runs --project . # what every run is doing right now
kage review --project . # read a finished run's claim and diff
kage merge <run-id> --project . # land the code and ratify what it learned
Every run works in its own git worktree. The checks that decide the verdict on the receipt above — tests, diff size, citations — are commands Kage runs itself, never the agent's self-report.
- The app.
kage app --project <dir>starts (or reuses) the local daemon and opens the same room, runs board, and memory view in a UI. From a checkout,npm start --prefix shellruns it as a native window — a thin Electron shell with no HTML of its own, it just loads the daemon's own page — andnpm run dmg --prefix shellbuilds a macOS.dmg(arm64 only; Windows/Linux packaging isn't built yet). - From your phone. The daemon can also bind to your machine's LAN address, gated by a
pairing secret required on every request, reads included. Today that means setting
"lan": truein.agent_memory/config.jsonby hand — there's no--lanflag or app toggle yet. - Add a project without a terminal.
kage projects add <dir> --agent clauderegisters another repo the same way the app's "+" button does, thenkage app --project <dir>opens it.
kage app --project <dir>
kage projects add <dir> --agent claude
Desktop app
A thin native shell (macOS, arm64 only) over the same daemon the CLI runs — dock presence,
a global hotkey, native notifications. Download the latest .dmg from
GitHub releases (look for a
Kage-<version>.dmg asset).
Unsigned builds show macOS's "unidentified developer" prompt on first launch — right-click the app in Finder and choose Open once. Once installed, it checks for updates on launch and every 4 hours and installs on restart; ad-hoc (unsigned) builds can't self-install and notify you instead, linking back to the releases page.
Prefer the CLI? The one-line install works everywhere the app doesn't need to:
npx -y @kage-core/kage-graph-mcp install
What is Kage
Kage is an orchestrator for coding agents, built on a memory layer. As your agent works, it captures what it learns
(decisions, bug fixes, conventions, how the code fits together) as
Open Knowledge Format (OKF)
concept files committed in your repo under .agent_memory/. The next session (yours or a
teammate's) starts already knowing it, instead of re-reading or re-asking.
Three things make it different from other memory tools:
- It's collaborative. The knowledge one person (or their agent) figures out becomes the whole team's. Memory is shared through git, so a teammate's next session starts with what you just learned, not a blank slate.
- It's standard & git-native. Memory is a conformant OKF bundle — plain Markdown in your repo, reviewed in the same PR as the code, readable by any OKF tool — not locked in one machine or a vendor's cloud. Your knowledge stays yours.
- It's verified. Every memory cites the code it's about, and Kage checks those citations against your actual files at write time, at recall time, and when a diff changes the code. Memory that no longer matches the code is withheld, so the agent never acts on a stale claim.
Kage called it. Google standardized it.
From day one, Kage kept agent memory as plain files in your repo — no cloud, no database, no lock-in, while everyone else was building memory clouds. In June 2026, Google Cloud shipped the Open Knowledge Format: knowledge as Markdown in git, vendor-neutral, no account — the exact thesis Kage already ran on. So Kage adopted OKF as its standard, and supercharges it with the layer OKF deliberately leaves out:
- Verification — OKF stores what you wrote down; Kage checks every concept against your real code and refuses hallucinated citations at write time.
- Freshness — OKF has no notion of staleness; Kage catches drift the moment your code changes and withholds memory that's no longer true.
- Code-grounding — a deterministic code graph anchors each concept to the exact symbols it describes — the layer OKF leaves to tooling.
The trust metadata rides in OKF-legal x-kage-* fields, so a Kage bundle stays 100%
conformant and opens in any OKF consumer, including Google's own visualizer.
OKF standardizes the store; Kage is the verification and freshness layer Google left out.
How it works
Once installed, it's ambient. You don't run anything by hand:
- Recall before acting. At the start of a task (and the moment the agent opens a file), Kage surfaces the relevant verified memory for it. Stale or deleted memory is left out.
- Capture as it works. Durable learnings become packets. A memory that cites a file which doesn't exist is rejected on the spot, so hallucinations never enter storage.
- Stay honest as the code moves. When a diff changes code that a memory cites, that
memory is flagged at commit/PR time (
kage pr check) and withheld from recall until it's re-verified or replaced, so knowledge can't quietly rot.
Watch it happen in the local dashboard (kage viewer): packets, the memory↔code graph,
trust gates, and live events stream in as the agent works. Wrap anything in
<private>…</private> and it's never stored.
Why Kage
Most memory tools (claude-mem, agentmemory, mem0, Zep) store memory per-machine or in a cloud you don't own, and never re-check it against the code. Kage keeps it in your repo and verifies it, so it stays your team's and stays true as the code changes.
| | Kage | claude-mem | mem0 / Zep |
|---|---|---|---|
| Automatic capture + session-start recall | ✓ | ✓ | via SDK |
| Hallucinated citations rejected at write time | ✓ | — | — |
| Stale memory withheld at recall (cited files deleted/changed, TTL, reported) | ✓ | — | — |
| Diff-time stale-catch, warned before the PR when your change breaks a memory | ✓ | — | — |
| Memory reviewed in git, same PR as the code (plain files, no DB) | ✓ | SQLite + cloud | hosted API |
| Codify memory into team SKILL.md files agents auto-load | ✓ (kage skills) | — | — |
| Cross-mach
Truncated for display — read the full file on GitHub.
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Languages
Trust signals
From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.
