Alternatives
Products that do what Harbor does
CLI + companion App to spin up complete local LLM stacks
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- 11RA
Hi HN folks, I have been building AI agents for quite some time now. The shift has gone from LLM + Tools → LLM Workflows → Agent + Tools + Memory, and now we are finally seeing true agency emerge: agents as systems composed of tools, command-line access, fine-grained system capabilities, and memory. This way of building agents is powerful, and I believe it is here to stay. But the real question is: are the systems powering these agents ready for that future? I do not think so. Using Docker for a single agent is not going to scale well, because agents need to be lightweight and fast. LLMs…
Mar 2026 · github.com
- 12LS
LLMStack is a low-code platform that can be used to build LLM apps, chatbots and integrate AI experiences into existing products/workflows. It comes with everything out of the box that one needs to build LLM apps locally. It can also be used in a multi-tenant setting, making it available for everyone to use in an enterprise. Some highlights of the platform: - Chain multiple LLM models allowing for complex pipelines - Includes a vector database and necessary connectors to help enrich LLM responses with private data - App templates tailored to specific use cases to quickly build LLM apps…
2023 · github.com
- 13AC
Hi HN, we're Ashpreet, Eli and Yash and we're excited to share Phidata: a collection of AI Apps built with open-source tools. While helping teams build AI products, we built templates for spinning up LLM Apps quickly. Today we're open-sourcing our templates for building: - RAG LLM Apps - Autonomous LLM Apps - Multimodal LLM Apps - Data Engineering LLM Apps Templates are built with FastApi for serving, Streamlit for prototyping, PgVector for vectors and PosgreSQL for storage. Run them locally using docker and in production on AWS - with 1 command. - Github:…
2023 · github.com
- 14CF
Hey everyone! For the past two weeks my friend and I have been heads-down building Cloi, a fully local debugging agent that runs right in your terminal. You probably know the drill—every AI coding tool asks for API keys, subscriptions, and uploads your entire codebase to the cloud. Cloi does none of that: it runs entirely on your machine, with no cloud, no API keys, no subscriptions, and zero data leaving your system. What Cloi does: - Contextual error capture: Grabs your stack trace, local files, and environment to understand the issue. - Local LLM inference: Spins up Ollama on your box and…
2025 · github.com
- 15LN
npm for LLMs — install, run, and share AI models. We’ve built llmpm, a CLI tool that makes open-source LLMs installable like packages. llmpm install llama3 llmpm run llama3 You can also package models with your projects so others can reproduce the same setup easily. Website: https://llmpm.co GitHub:https://github.com/llmpm/llmpm-dev
Mar 2026 · llmpm.co
- 16UL
Hi everyone, Just wanted to share a use case where local LLMs are genuinely helpful for daily workflows: file organization. I've been working on a C++ desktop app called AI File Sorter – it uses local LLMs via `llama.cpp` to help organize messy folders like `Downloads` or `Desktop`. Not sort files into folders solely based on extension or filename patterns, but based on what each file actually is supposed to do or does. Basically: what would normally take me a great deal of time for dragging and sorting can now be done in a few. It's cross-platform (Windows/macOS/Linux), and fully…
2025 · github.com
- 17IB
hey hn, I built an open-source Perplexity clone that can run local LLMs and cloud LLMs. It's fully self-hostable through Docker and uses ollama to support local LLMs. The demo video in the repository shows me running it locally with llama3 on my M1 Macbook Pro. I'm open to any suggestions or feedback, thanks!
2024 · github.com
- 18LC
2023 · simonwillison.net
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- 20AG
I’ve been building LLM tooling for a small VC fund and found myself explaining the same mental model over and over to non-technical people around me: how a stateless LLM becomes a chatbot, how tool use works, what an agent is mechanically, and why context windows shape all of it. I never found a guide that covered that full chain at the level I wanted, so I wrote one. It’s nine short chapters, each building on the last. Deliberately simplified: the goal is a useful mental model, not a textbook. Feedback, corrections, and contributions welcome: github.com/ymyke/aiaiai
Apr 2026 · aiaiai.guide
- 21LF
I've been building agentic apps for some large Fortune 500 companies (T-Mobile, Twilio, etc.) and developed a mental model that serves as a practical guide in building agentic apps: separate the high-level agent specific logic from low-level platform capabilities. I call it the L-MM: the Logical Mental Model for LLM applications. This mental model has not only been tremendously helpful in building agents but also helping customers think about the development process - so when I am done with a consulting engagement they can move faster across the stack and enable engineers and platform teams…
2025
- 22CM
Hey HN, I've been building AutoAgents, an AI agent framework in Rust. Today I'm sharing a feature I haven't seen done well elsewhere: composable middleware layers for LLM inference pipelines. The problem Every agent framework lets you swap LLM providers. Almost none of them give you a structured way to enforce safety, caching, or data sanitization in the inference path itself. You end up with guardrails as application-level if-statements, caching bolted on as a separate service, and PII handling as a "we'll add it later" TODO that never ships. This gets worse with local models. Cloud APIs…
Mar 2026 · github.com
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2025 · github.com
- 24HA
Demo starts at 50m into the video. This was a bit terrifying to record because 2am the previous night everything was totally broken after a major refactor (so that we could add external LLM support as well as local GPUs). But pressure can be a useful force :-D We start with a stack deployed on my laptop without a GPU, pointing to together.ai so we can run open source LLMs easily without having to have access to a GPU. We show simple inference through the ChatGPT-like web interface (with users, sessions etc) and then simple drag'n'drop RAG. Then we show some helix apps defined as yaml: Marvin…
2024 · youtube.com
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