by kayba-ai
🔨 Kyoko is the all-in-one, fully local tool for debugging and improving your AI agents.
# Add to your Claude Code skills
git clone https://github.com/kayba-ai/KyokoKyoko is an open-source ai agents skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by kayba-ai. 🔨 Kyoko is the all-in-one, fully local tool for debugging and improving your AI agents. It has 53 GitHub stars.
Kyoko's catalog security scan is still queued. You can run an instant dependency and prompt-injection check now with the "Scan for vulnerabilities" button above.
Clone the repository with "git clone https://github.com/kayba-ai/Kyoko" and add it to your Claude Code skills directory (see the Installation section above).
Kyoko is primarily written in Python. It is open-source under kayba-ai on GitHub, so you can review or fork the full source.
Yes. SkillsLLM lists many other AI Agents skills you can browse and compare side by side. Open the AI Agents category from the badge at the top of this page, or use the Related Skills and comparison links further down to weigh Kyoko against similar tools.
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Unlocks once the catalog security scan passes (runs nightly).
The deep catalog scan for this skill is still queued. Run an instant dependency check now instead.
Kyoko is a fully local system for measuring, debugging, and improving AI agents.
Add telemetry, run your agent. Kyoko shows where performance breaks across runs. It groups recurring failures into evidence-backed issues, lets Codex or Claude Code draft fixes, and only applies changes after checks and evals pass.
Built around the manual dev workflow. Inspect traces, understand the failure, patch the prompt, context, or harness, rerun evals, and decide what ships. Kyoko makes that workflow repeatable while keeping you in control.
Local by default. Traces, issues, proposals, evals, database, and dashboard stay on your machine.
Works with your existing coding-agent subscription. Kyoko can use the Codex or Claude Code CLI you already have, so there is no separate Kyoko model API key or hosted service.
┌─────────────────┐ ┌─────────────────┐
│ 1. Analyse │ ───────▶ │ 2. Issues │
│ traces in │ │ recurring │
│ │ │ failures │
└─────────────────┘ └─────────────────┘
▲ │
│ measure │ accept
│ ▼
┌─────────────────┐ ┌──────┐ ┌─────────────────┐
│ 4. Evals │◀─┤ gate ├─│ 3. Proposals │
│ failure rate │ └──────┘ │ fixes │
│ │ apply │ │
└─────────────────┘ └─────────────────┘
Kyoko keeps the repair loop explicit. Every step creates something you can inspect in the dashboard or CLI.
The gate is the control point. It applies a fix only when checks, replay evidence, autonomy policy, and human locks allow it.
Run it your way. The same loop, the same gate. You pick the autonomy level:
Try Kyoko without wiring up an agent. The demo creates a local database, loads bundled fixture runs, and serves the dashboard.
pipx install kyoko
kyoko demo --db /tmp/kyoko-demo.db --json
kyoko serve --db /tmp/kyoko-demo.db
Open http://127.0.0.1:8765.
Requires Python 3.12 or newer. No live model, framework adapter, or replay server is needed for the demo.
From the root of your agent project (e.g. the repo of your AI agent, Hermes or Openclaw), needs Python 3.12+:
pipx install kyoko
kyoko project-bootstrap
kyoko serve
Open http://127.0.0.1:8765. pip install kyoko and
uv tool install kyoko work too; see docs/INSTALL.md.
Bootstrap writes a local .kyoko/ workspace: database, scaffolds, MCP config,
and operator presets. Every later kyoko command finds that database
automatically, so no --db flags are needed inside your project.
Then wire up telemetry. This is the step that makes everything else work: Kyoko can only find and fix what it can see. The easiest way is to let your coding agent do the wiring:
kyoko install-skill # then run /kyoko-instrument in your coding agent
This installs the bundled /kyoko-instrument skill into .claude/skills/ and
.agents/skills/, where Claude Code and Codex pick it up automatically; for
Cursor or other agents, kyoko install-skill --print prints the same playbook
to paste in.
The skill finds your agent's entry point, records one real run, and verifies
it shows up in Kyoko.
To connect your agent over MCP instead, or to wire telemetry by hand (Python or TypeScript SDK, OTLP, importers), see Getting Started.
LearningProposal
records for context, skills, evals, o