> For the complete documentation index, see [llms.txt](https://docs.zenml.io/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.zenml.io/user-guides/readme.md).

# Overview

Discover how to build production-ready ML pipelines and production-grade AI agents with ZenML and Kitaru through our curated learning resources. Whether you're looking for step-by-step instructions, complete project implementations, or specific examples, you'll find resources to accelerate your workflow.

[Kitaru](https://docs.zenml.io/kitaru) is ZenML's sibling project for production AI agents: **run, replay, improve**. Every model call and tool call in a run is recorded as a durable checkpoint, so you can replay a real run with one thing changed (a different model or prompt), diff the two, then roll the winning change across a cohort of recent runs. A Kitaru flow is a dynamic ZenML pipeline under the hood, so agents and pipelines share the same stacks, server, and dashboard. It has its own learning track below.

## Guides

Step-by-step instructions to help you master ZenML concepts and features.

<table data-view="cards"><thead><tr><th></th><th></th><th data-hidden data-card-cover data-type="files"></th><th data-hidden data-card-target data-type="content-ref"></th></tr></thead><tbody><tr><td><strong>Starter Guide</strong></td><td>Get started with ZenML fundamentals and set up your first pipeline</td><td><a href="/https/docs.zenml.io/files/6Dju13U5TGcdl2hEtXIv">/files/6Dju13U5TGcdl2hEtXIv</a></td><td><a href="/https/docs.zenml.io/pages/d3v22nlU73h6GvDkQIJ5">/pages/d3v22nlU73h6GvDkQIJ5</a></td></tr><tr><td><strong>Production Guide</strong></td><td>Move your ML pipelines from development to production</td><td><a href="/https/docs.zenml.io/files/stANILxKZ2dcQqUSNPN0">/files/stANILxKZ2dcQqUSNPN0</a></td><td><a href="/https/docs.zenml.io/pages/t8IhQrN2VDhN5dkZwVm5">/pages/t8IhQrN2VDhN5dkZwVm5</a></td></tr><tr><td><strong>LLMOps Guide</strong></td><td>Build and deploy Large Language Model pipelines</td><td><a href="/https/docs.zenml.io/files/vpjTgWJkpGGCcGO0TF6l">/files/vpjTgWJkpGGCcGO0TF6l</a></td><td><a href="/https/docs.zenml.io/pages/acKBOqmrDTmtiWQWZbrH">/pages/acKBOqmrDTmtiWQWZbrH</a></td></tr><tr><td><strong>Agents Guide</strong></td><td>Run, replay, and improve AI agents with Kitaru: record runs as durable checkpoints, replay a real run with one change and diff it, then scale the winning change across a cohort.</td><td><a href="/https/docs.zenml.io/files/vpjTgWJkpGGCcGO0TF6l">/files/vpjTgWJkpGGCcGO0TF6l</a></td><td><a href="/https/docs.zenml.io/pages/HCJ1bcsvy2OWCoaWxSMO">/pages/HCJ1bcsvy2OWCoaWxSMO</a></td></tr><tr><td><strong>Tutorials</strong></td><td>Deep dives into advanced topics</td><td><a href="/https/docs.zenml.io/files/stANILxKZ2dcQqUSNPN0">/files/stANILxKZ2dcQqUSNPN0</a></td><td><a href="/https/docs.zenml.io/pages/WG1CHN0n24bzVe71EujG">/pages/WG1CHN0n24bzVe71EujG</a></td></tr></tbody></table>

## Projects

Complete end-to-end implementations that showcase ZenML in real-world scenarios.\
[See all projects in our website →](https://www.zenml.io/projects)

<table data-view="cards"><thead><tr><th></th><th></th><th data-hidden data-card-cover data-type="files"></th><th data-hidden data-card-target data-type="content-ref"></th></tr></thead><tbody><tr><td><strong>FloraCast</strong></td><td>A production-ready MLOps pipeline for time series forecasting using ZenML and Darts, featuring TFT-based training and scheduled batch inference.</td><td><a href="/https/docs.zenml.io/files/qEelRzBk1O8rnVE2gJ6t">/files/qEelRzBk1O8rnVE2gJ6t</a></td><td><a href="https://www.zenml.io/projects/floracast">https://www.zenml.io/projects/floracast</a></td></tr><tr><td><strong>LLM-Complete Guide</strong></td><td>Production-ready RAG pipelines from basic retrieval to advanced LLMOps with embeddings finetuning and evals.</td><td><a href="/https/docs.zenml.io/files/zr7f4ZUlaFhyT6bJbAaY">/files/zr7f4ZUlaFhyT6bJbAaY</a></td><td><a href="https://github.com/zenml-io/zenml-projects/tree/main/llm-complete-guide">https://github.com/zenml-io/zenml-projects/tree/main/llm-complete-guide</a></td></tr><tr><td><strong>Retail Forecast</strong></td><td>A robust MLOps pipeline for retail sales forecasting designed for retail data scientists and ML engineers.</td><td><a href="/https/docs.zenml.io/files/BTWokilljnwihmCsiYiY">/files/BTWokilljnwihmCsiYiY</a></td><td><a href="https://www.zenml.io/projects/retail-forecast">https://www.zenml.io/projects/retail-forecast</a></td></tr><tr><td><strong>Research Radar</strong></td><td>Automates research paper discovery and classification for specialized research domains.</td><td><a href="/https/docs.zenml.io/files/37H7dpGYa60GWnXASZXU">/files/37H7dpGYa60GWnXASZXU</a></td><td></td></tr><tr><td><strong>OncoClear</strong></td><td>A production-ready MLOps pipeline for accurate breast cancer classification using machine learning.</td><td><a href="/https/docs.zenml.io/files/LwPakXes63vdOPgla2CW">/files/LwPakXes63vdOPgla2CW</a></td><td><a href="https://www.zenml.io/projects/oncoclear">https://www.zenml.io/projects/oncoclear</a></td></tr><tr><td><strong>Sign Language Detection with YOLOv5</strong></td><td>End-to-end computer vision pipeline</td><td><a href="/https/docs.zenml.io/files/MhhoRXjDKRepI3nipR4F">/files/MhhoRXjDKRepI3nipR4F</a></td><td><a href="https://www.zenml.io/projects/sign-language-detection-with-yolov5">https://www.zenml.io/projects/sign-language-detection-with-yolov5</a></td></tr><tr><td><strong>ZenML Support Agent</strong></td><td>A production-ready agent that can help you with your ZenML questions.</td><td><a href="/https/docs.zenml.io/files/5fc2ssf3kSj3Zn2vatKP">/files/5fc2ssf3kSj3Zn2vatKP</a></td><td><a href="https://www.zenml.io/projects/zenml-support-agent">https://www.zenml.io/projects/zenml-support-agent</a></td></tr><tr><td><strong>OmniReader</strong></td><td>A scalable multi-model OCR workflow framework for batch document processing and model evaluation.</td><td><a href="/https/docs.zenml.io/files/9gj3RLbQFgxjSRh9fepk">/files/9gj3RLbQFgxjSRh9fepk</a></td><td><a href="https://www.zenml.io/projects/omnireader">https://www.zenml.io/projects/omnireader</a></td></tr><tr><td><strong>EuroRate Predictor</strong></td><td>Turn European Central Bank data into actionable interest rate forecasts with this comprehensive MLOps solution.</td><td><a href="/https/docs.zenml.io/files/10TfvmeDPmvQHaUXn54G">/files/10TfvmeDPmvQHaUXn54G</a></td><td><a href="https://www.zenml.io/projects/eurorate-predictor">https://www.zenml.io/projects/eurorate-predictor</a></td></tr></tbody></table>

## Examples

Focused code snippets and templates that address specific ML workflow challenges.\
[See all examples in GitHub →](https://github.com/zenml-io/zenml-projects)

<table data-view="cards"><thead><tr><th></th><th></th><th data-hidden data-card-cover data-type="files"></th><th data-hidden data-card-target data-type="content-ref"></th></tr></thead><tbody><tr><td><strong>Quickstart</strong></td><td>Bridging Local Development and Cloud Deployment</td><td><a href="/https/docs.zenml.io/files/uXan5TkgPMOmnavZPOmy">/files/uXan5TkgPMOmnavZPOmy</a></td><td><a href="https://github.com/zenml-io/zenml/blob/main/examples/quickstart/README.md">https://github.com/zenml-io/zenml/blob/main/examples/quickstart/README.md</a></td></tr><tr><td><strong>End-to-End Batch Inference</strong></td><td>Supervised ML project built with the ZenML framework and its integration.</td><td><a href="/https/docs.zenml.io/files/v5bEkxsmxUpju2HbBxsx">/files/v5bEkxsmxUpju2HbBxsx</a></td><td><a href="https://github.com/zenml-io/zenml/tree/main/examples/e2e">https://github.com/zenml-io/zenml/tree/main/examples/e2e</a></td></tr><tr><td><strong>Agent Architecture Comparison</strong></td><td>Compare AI agents with LangGraph workflows, LiteLLM integration, and automatic visualizations.</td><td><a href="/https/docs.zenml.io/files/9GCulceisdM2OHP9bX6x">/files/9GCulceisdM2OHP9bX6x</a></td><td><a href="https://github.com/zenml-io/zenml/blob/main/examples/agent_comparison/README.md">https://github.com/zenml-io/zenml/blob/main/examples/agent_comparison/README.md</a></td></tr><tr><td><strong>Agent Framework Integrations</strong></td><td>Production-ready integrations for 11 popular agent frameworks including LangChain, CrewAI, AutoGen, and more.</td><td><a href="/https/docs.zenml.io/files/9GCulceisdM2OHP9bX6x">/files/9GCulceisdM2OHP9bX6x</a></td><td><a href="https://github.com/zenml-io/zenml/tree/main/examples/agent_framework_integrations">https://github.com/zenml-io/zenml/tree/main/examples/agent_framework_integrations</a></td></tr><tr><td><strong>Deploying Agents</strong></td><td>Document analysis service with pipelines, evaluation, and embedded web UI.</td><td><a href="/https/docs.zenml.io/files/nScfQAHH4kOEG30n8cdP">/files/nScfQAHH4kOEG30n8cdP</a></td><td><a href="https://github.com/zenml-io/zenml/blob/main/examples/deploying_agent/README.md">https://github.com/zenml-io/zenml/blob/main/examples/deploying_agent/README.md</a></td></tr><tr><td><strong>Agent Outer Loop</strong></td><td>Agent training and evaluation loop: evolve a generic agent into a specialized support system through intent classification and model training.</td><td><a href="/https/docs.zenml.io/files/nScfQAHH4kOEG30n8cdP">/files/nScfQAHH4kOEG30n8cdP</a></td><td><a href="https://github.com/zenml-io/zenml/blob/main/examples/agent_outer_loop/README.md">https://github.com/zenml-io/zenml/blob/main/examples/agent_outer_loop/README.md</a></td></tr><tr><td><strong>Basic NLP with BERT</strong></td><td>Build NLP models with production-ready ML pipeline framework</td><td><a href="/https/docs.zenml.io/files/XU0XOjRrN8OM07jhbKax">/files/XU0XOjRrN8OM07jhbKax</a></td><td><a href="https://github.com/zenml-io/zenml/tree/main/examples/e2e_nlp">https://github.com/zenml-io/zenml/tree/main/examples/e2e_nlp</a></td></tr><tr><td><strong>Computer Vision with YoloV8</strong></td><td>End-to-end computer vision pipeline with modular design</td><td><a href="/https/docs.zenml.io/files/UHEigYIIdjJmLcnfY3QH">/files/UHEigYIIdjJmLcnfY3QH</a></td><td><a href="https://github.com/zenml-io/zenml/tree/main/examples/computer_vision">https://github.com/zenml-io/zenml/tree/main/examples/computer_vision</a></td></tr><tr><td><strong>LLM Finetuning</strong></td><td>LLM fine-tuning pipeline with PEFT approach</td><td><a href="/https/docs.zenml.io/files/xVdGBDAkOz29s76Iy6vX">/files/xVdGBDAkOz29s76Iy6vX</a></td><td><a href="https://github.com/zenml-io/zenml/tree/main/examples/llm_finetuning">https://github.com/zenml-io/zenml/tree/main/examples/llm_finetuning</a></td></tr><tr><td><strong>Durable Agent Platform (Kitaru)</strong></td><td>The runnable code behind the Agents guide: an internal agent harness platform with Kitaru and PydanticAI.</td><td></td><td><a href="https://github.com/zenml-io/kitaru/tree/develop/examples/end_to_end/agent_harness_platform">https://github.com/zenml-io/kitaru/tree/develop/examples/end_to_end/agent_harness_platform</a></td></tr></tbody></table>

<figure><img src="https://static.scarf.sh/a.png?x-pxid=f0b4f458-0a54-4fcd-aa95-d5ee424815bc" alt="ZenML Scarf"><figcaption></figcaption></figure>


---

# Agent Instructions
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## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter:

```
GET https://docs.zenml.io/user-guides/readme.md?ask=<question>&goal=<endgoal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
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