This release is a pre-release and may not be stable for production use.
Traces you can run, not just read.
Kitaru (来る, "to arrive") is replay-based evals for AI agents. It turns your agent's production traces into a regression suite: record or import every run, replay it against your next change, and see what improved and what broke before you ship. Open source, self-hosted, Python and TypeScript. From the team behind ZenML.
Docs · Get Started · Roadmap · Community
🎯 Why
Your agent has already been tested thousands of times, in production. Every one of those runs is sitting in a trace store as a transcript you can read but not run. So you change a prompt, swap a model, refactor a tool, and find out whether it broke from an angry user.
Kitaru makes those traces runnable:
- Every run is a recording. Wrap your agent once, or import the traces you already collect from Langfuse, LangSmith, Braintrust, or Logfire. Your trace store stays your system of record.
- Replay is re-execution. Your real code runs again, tool calls answered from the recording, so no card gets refunded twice. Unchanged, the replay reproduces the original; forked, the diff you see is your change.
- Improvement is measured, not vibed. Your coding assistant interviews you over the sessions that matter, your judgments calibrate evaluators, and experiments replay a frozen cohort against your change: what improved, what regressed, before it ships. The cohort that caught a failure becomes the regression gate that keeps it caught.
⚡ Get started
1. Install and log in. The local server (FastAPI + Postgres) provisions itself in Docker:
uv add "kitaru[cli,worker,mcp]" kitaru-pydantic-ai # or: pip install
kitaru login --local # or: kitaru login <your-team-url>
2. Make your coding assistant Kitaru-capable. This is the intended way to drive it: skills teach it the method, the MCP server gives it bounded operations.
npx skills add zenml-io/kitaru-skills
{
"mcpServers": {
"kitaru": {
"command": "uv",
"args": ["run", "kitaru-mcp", "--server", "http://localhost:8000", "--mode", "standard"]
}
}
}
3. Get an agent with traces. The fastest way to feel the loop is the template: a ready PydanticAI agent with checked-in Langfuse traces to investigate.
git clone https://github.com/zenml-io/kitaru-template
cd kitaru-template # its README has the setup and import commands
Already have an agent in production? Import its traces from Langfuse, LangSmith, Braintrust, or Logfire, or record with an adapter: one wrapper, no rewrite.
4. Drive results. Open the repo (the template or your own agent) in Claude Code, Codex, or Cursor and ask:
Use kitaru-investigation to investigate this agent and help me test one meaningful improvement. Show me the recorded evidence before asking for a judgment, and ask before creating resources or starting paid replay.
The assistant sweeps the sessions with the built-in deterministic evaluators, interviews you over the ones that matter, drafts the evaluator, runs the experiment, and reports what improved and what regressed. You judge; it does the legwork. The tutorial walks the same loop on the template, step by step.
🔌 Languages and frameworks
Python and TypeScript SDKs, one server. Adapters:
| Framework | Language | Package |
|---|---|---|
| PydanticAI | Python | kitaru-pydantic-ai |
| LangGraph (also LangChain agents, Deep Agents) | Python | kitaru-langgraph |
| OpenAI Agents SDK | Python | kitaru-openai-agents |
| Mastra | TypeScript | @zenml-io/kitaru-mastra |
| Vercel AI SDK | TypeScript | @zenml-io/kitaru-vercel-ai |
Anything else still works: import your traces, write a one-page custom importer (an agent skill drafts it), or build a small adapter.
🔒 Self-hosted, by design
One server on your infrastructure (Docker image and Helm chart included), and no user code executes on it. Replays, imports, and evaluations run on workers in your environment: your virtualenv, your credentials, your network. Traces never have to leave your systems. Apache 2.0, no mandatory SaaS control plane.
📚 Learn more
| Resource | Description |
|---|---|
| Documentation | Concepts, guides, and the quickstart |
| Quickstart | From an agent in production to your first replay-backed decision |
| Set up your coding agent | Install the MCP server and the agent skills |
| Kitaru template | A ready agent and traces to try the method on |
| Import your traces | Langfuse, LangSmith, Braintrust, Logfire, or any format |
| Build a regression suite | Production traffic as your test suite, gated in CI |
| Deploy Kitaru | Self-host for your team |
🤝 Contributing
We welcome contributions! See CONTRIBUTING.md for development setup, code style, and how to submit changes. The default branch is develop; all PRs should target it.
💬 Community and support
- Slack community: ask questions, share ideas
- Report a bug: goes straight to GitHub issues
- support@kitaru.ai: when email is easier
Hit something broken? Any of the three reaches a human; an issue with a session ID attached gets fixed fastest.
📄 License
1 maintainer
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