ollama-playground: A Collection of 25 Runnable LLM Projects Using Local Ollama Models
Interesting LLM projects that I created for my YouTube channel using Ollama's open-source models.
At a glance
- What is it?
- ollama-playground is a Python code repository by Nariman Codes that pairs each of its 25 self-contained LLM projects with a companion YouTube video walkthrough. It covers RAG pipelines, agent tooling protocols, multi-agent architectures, voice, and vision, all built against Ollama's locally-running open-source models. It is aimed at developers who want working code they can run immediately and study alongside a tutorial.
- Who is it for?
- ollama-playground suits developers who are learning to build with local LLMs and want a collection of complete, runnable starting points rather than documentation fragments. It is not a framework or a library; importing it as a dependency makes no sense.
- Can I use it commercially?
- Yes. MIT is a permissive licence: you can use, modify and sell software built on it, as long as you keep its copyright and licence notices.
- Is it still maintained?
- Yes. The repository last received commits 23 days ago.
- What is it written in?
- Mainly Python, according to GitHub's language statistics.
Answers come from the project's GitHub data, last synced on September 29, 2026, and from our analysis. They are not legal advice.
Editorial analysis
Who ollama-playground Is For and What It Provides
ollama-playground is explicitly a companion resource for the @NarimanCodes YouTube channel. The README states its purpose plainly: the repository contains code for the projects built using Ollama's open-source models for the channel. A developer who wants to follow along with tutorials and run the code locally is the intended user.
The value is in the breadth of covered topics. Across 25 projects, the repository spans retrieval-augmented generation over PDFs, autonomous web scraping agents, multi-agent investment advisors, text-to-SQL pipelines, an AI podcast generator, and object detection. Every project uses Ollama models running locally, which means no API keys, no network dependency for inference, and no per-token billing during development.
How the Repository Is Organized
The top-level README groups the 25 projects into six categories. The RAG category includes four projects: Chat with PDFs, Hybrid RAG for PDFs, Multimodal RAG for PDFs, and Voice RAG. Each lives in its own directory with its own README.md.
The Agent Tooling and Protocols category covers five projects: Agent with Memory, MCP-Based Agent using the GPT-OSS model, Secure MCP Server and Client, ACP-Based Agents, and a recreation of Karpathy's LLM Wiki. These reflect the protocols that have emerged for connecting LLMs to external tools.
Practical Agents contains Web Scraping Agent, Research Assistant Agent, and Text-to-SQL Agent. The Multi-Agent Systems category has two projects: a Multi-Agent Researcher using a supervisor architecture, and a Multi-Agent Investment Advisor using a swarm architecture. The Voice category contains an AI Podcast Generator and a Local Voice Assistant. The Vision category wraps up with Video Summarization Agent, OCR Agent, Emotion Detection Agent, Object Detection Agent, and Image Search Engine.
Each project directory contains its own README.md with project-specific setup instructions. The top-level README links to each subdirectory's README, making navigation straightforward.
Cloning and Running a Project
There is no global install script at the repository root. Setup is per-project: clone the repository, navigate into the chosen project directory, and follow that project's README.
git clone https://github.com/NarimanN2/ollama-playground.git
cd ollama-playgroundFrom there, navigate to the specific project. For example, to work on the PDF chat project:
cd chat-with-pdfEach project directory has its own README.md. Because the YouTube video companion is the intended companion to the code, the per-project README may be brief; the video provides the context that a standalone README does not.
All projects depend on Ollama being installed and running locally. Ollama serves open-source models through a local API endpoint, so the projects call that endpoint rather than a remote provider. This means the first step for any project is ensuring Ollama is installed and the required model is downloaded through it.
What This Repository Leaves Out
ollama-playground is a collection of demonstration projects, not a production-grade system. Each project is self-contained and designed to illustrate a specific pattern. There is no shared library, no common configuration layer, and no cross-project test suite. A developer who wants to take one of these patterns into production needs to harden it: add error handling, proper logging, secret management, and scalability concerns that the demonstration code omits.
The repository has no GitHub releases and no changelog. When the author updates a project, there is no versioned path for existing users. Developers who pin a dependency on a specific state of this repository should do so by commit hash.
Five of the 25 projects in the Vision and Voice categories depend on models and libraries with system-level requirements (such as audio capture or camera access) that may require additional setup on headless servers. The per-project READMEs document these, but the top-level README does not summarize them.
Comparison with Semantic Kernel
Semantic Kernel (github.com/microsoft/semantic-kernel) is a widely known AI orchestration SDK from Microsoft. It provides connectors, memory, agents, and plugin abstractions designed for building production applications across multiple LLM providers. It runs on .NET, Python, and Java and integrates with the Azure and Microsoft 365 ecosystems.
The difference is purpose and depth. Semantic Kernel is an SDK you add as a dependency and build on top of; it ships APIs, not runnable examples. ollama-playground is a collection of complete runnable projects; it ships application code, not a library. A developer learning to build agents with local models will find more immediate running code in ollama-playground, but will hit the ceiling of what demonstration code teaches before reaching production complexity. Semantic Kernel handles production integration patterns, cross-provider portability, and enterprise authentication that ollama-playground does not address.
Maintenance, Licence, and YouTube Context
The last push to the repository was on 2026-09-07. The repository is licensed under the MIT licence, which allows free use, modification, and redistribution including in commercial products. The repository has no contributor licence agreement and no code of conduct file at the top level.
The README explicitly asks readers to subscribe to the @NarimanCodes YouTube channel for more content. The pairing between this repository and the video content is central to how the author intends it to be used. Developers who skip the videos and work only from the code may find the READMEs in individual project directories insufficient for full context, particularly for the newer protocols such as ACP-Based Agents and the Secure MCP Server projects, where the configuration choices and security model are not fully documented in the code alone.
The repository covers 25 projects, and the breadth suggests an ongoing series rather than a finished collection. New projects are likely to appear as the YouTube channel publishes new videos.
Editorial conclusion
ollama-playground suits developers who are learning to build with local LLMs and want a collection of complete, runnable starting points rather than documentation fragments. It is not a framework or a library; importing it as a dependency makes no sense. Teams looking for production-grade LLM orchestration with enterprise tooling should look at dedicated frameworks. The right way to use this repository is to pick the project closest to your use case, clone the repository, and follow the companion YouTube video for setup context that the README alone does not provide. The last push to the repository was on 2026-09-07.
Frequently asked questions
How is ollama-playground organized and which project should I start with?
The repository groups 25 projects into six categories: RAG, Agent Tooling and Protocols, Practical Agents, Multi-Agent Systems, Voice, and Vision. Each lives in its own subdirectory with its own README.md. For a first project, the Chat with PDFs project in the chat-with-pdf directory is documented first in the README and covers basic RAG, which is a prerequisite pattern for many of the others.
Does ollama-playground require an internet connection to run the models?
The projects call Ollama's local API endpoint, which runs models on your own machine. Once a model is downloaded through Ollama, inference runs offline. The initial model download does require an internet connection, but subsequent runs do not.
Is ollama-playground a library or framework I can add as a dependency?
No. ollama-playground is a collection of standalone runnable projects, not a library. There is no package published to PyPI, no shared API, and no install script at the repository root. The correct use is to clone the repository and run individual projects as applications.
Official sources
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