# Jan vs RLAMA Jan records 291,894 monthly visits with -9.6% month-over-month growth and Free pricing. RLAMA records 0 monthly visits with -100% growth and Freemium pricing. | Metric | [Jan](https://openaitools.app/tool/jan) | [RLAMA](https://openaitools.app/tool/rlama) | | :--- | :--- | :--- | | Website URL | https://jan.ai | https://rlama.dev | | Categories | AI Chatbot, AI Assistant, Open Source AI Models, Large Language Models (LLMs) | AI Assistant, AI Answer, AI Agent, Large Language Models (LLMs) | | Pricing Model | Free | Freemium | | Monthly Visits | 291,894 | 0 | | MoM Growth Rate | -9.6% | -100% | | Primary Market | United States | Global | | Direct Visits | 105,615 | 0 | | Review Score | 4.6 | 4.5 | | Saved Count | 6 | 0 | | Supported Platforms | Website | Website | ## Jan Jan is a desktop application that positions itself as an open replacement for ChatGPT, with the key difference that inference happens on hardware you control. After installing the app you pick a model, and Jan downloads and runs it locally, so prompts, chat history, and files stay on your machine rather than on someone else's server. For heavier workloads you can connect an OpenAI, Anthropic, Gemini, or Mistral key and route requests to the cloud while keeping the same chat interface and local history. The project is developed in the open by Menlo Research and has accumulated tens of thousands of GitHub stars along with millions of downloads. It exposes OpenAI-compatible local endpoints, which means existing tooling and SDKs can point at Jan instead of a hosted service, and it supports custom assistants with their own instructions and model choices. Recent releases have added an agent surface alongside the classic desktop chat, and a memory feature is announced as coming to carry context and preferences between sessions. Because it is open source, Jan can be audited, self-built, and extended, and there is no paid tier gating the desktop client. The practical tradeoff is that local model quality and speed depend entirely on your own GPU or CPU, so lighter machines will favor smaller quantized models while cloud keys remain the escape hatch for harder tasks. **Pricing & Plans:** Jan is free and open source, with no paid tier for the desktop app. Costs only appear if you optionally connect your own paid cloud API keys for providers such as OpenAI or Anthropic. - **Local-first inference:** Download and run open models such as Llama, Mistral, Qwen, Gemma, and DeepSeek directly on your own machine, with chat data kept on device. - **Bring your own cloud models:** Optionally connect API keys for OpenAI, Anthropic, Gemini, Mistral, and other providers to route requests to frontier models from the same interface. - **OpenAI-compatible local API:** Jan exposes a local server endpoint that follows the OpenAI API shape, so existing SDKs and tools can talk to your local models without code changes. - **Custom assistants:** Create assistants with their own system instructions, model selection, and parameters, then reuse them across conversations. - **Open source and extensible:** The full desktop client and agent code are public on GitHub, so you can audit behavior, build your own version, and contribute improvements. **Strengths:** Completely free and open source, with no account required to run local models; Conversation data stays on your device by default; Works across macOS, Windows, and Linux with an OpenAI-compatible local endpoint; Optional cloud key support lets you mix local and frontier models in one app **Limitations:** Local model quality and speed are capped by your own hardware; Very large models may need substantial RAM or VRAM to be practical; Some cloud-connected and memory features are newer or still rolling out [Profile (Markdown)](https://openaitools.app/tool/jan.md) ## RLAMA RLAMA started as a focused tool for turning a folder of documents into a queryable knowledge base from the shell. You point it at a directory, choose an embedding model, and RLAMA chunks, embeds and stores your files, then lets you run interactive chat sessions against the resulting RAG system. It supports plain text and Markdown as well as PDFs and common structured formats, and processes everything locally by default. The project has since grown into a broader agent platform. Beyond RAG creation you can define agents with roles and tool access (RAG search, web search, code execution), then orchestrate them as crews using sequential, parallel or hierarchical workflows. A watch mode keeps a RAG index in sync as files change, and an HTTP API server lets other applications query your systems programmatically. For people who prefer not to live in a terminal, RLAMA also offers a visual RAG builder that generates the equivalent command for you. The maintainers note that active development is currently paused while they balance full-time work and university studies, but the released CLI, documentation and installation packages remain available for macOS, Linux and Windows. **Pricing & Plans:** Free and open source to install from the project site, with an optional RLAMA-Pro tier announced on the homepage. Model inference itself depends on your local Ollama setup or on your own OpenAI/Hugging Face API credentials, which are billed by those providers. - **Local RAG pipelines:** Index folders of .txt, .md, .html, .pdf and other documents into searchable RAG systems with configurable semantic chunking strategies, stored and processed entirely on your machine. - **AI agents with tools:** Create specialized agents by role — researcher, writer, coder, analyst and more — and equip them with RAG search, web search or code execution. - **Multi-agent crews:** Combine agents into crews that collaborate through sequential, parallel or hierarchical workflows for multi-step automation tasks. - **Visual RAG builder:** Configure model, sources, exclusions and chunking settings in a browser interface and let RLAMA generate the matching CLI command for you. - **Automation and integration:** Watch a directory for new documents, sync incrementally, and expose any RAG or agent through the built-in HTTP API server on a port of your choosing. **Strengths:** Fully local processing keeps documents private by default; Combines RAG and multi-agent orchestration in one CLI; Cross-platform installers for macOS, Linux and Windows; Visual builder lowers the barrier for users who avoid the terminal **Limitations:** Homepage states active development is currently paused; Requires a working local model runtime such as Ollama for the best experience; Command-line first, so non-technical users may find setup fiddly [Profile (Markdown)](https://openaitools.app/tool/rlama.md) ## Frequently Asked Questions ### How do Jan and RLAMA compare in monthly traffic? Jan has 291,894 monthly visits, while RLAMA has 0. Traffic estimates describe audience size, not product quality. ### How do their growth rates compare? Jan has a month-over-month growth rate of -9.6%; RLAMA has -100%. ### How does pricing compare? Jan: Free. Jan is free and open source, with no paid tier for the desktop app. Costs only appear if you optionally connect your own paid cloud API keys for providers such as OpenAI or Anthropic. RLAMA: Freemium. Free and open source to install from the project site, with an optional RLAMA-Pro tier announced on the homepage. Model inference itself depends on your local Ollama setup or on your own OpenAI/Hugging Face API credentials, which are billed by those providers. ## Alternatives | # | Tool Name | Monthly Visits | MoM Growth Rate | Pricing Model | Summary | Profile (Markdown) | | :--- | :--- | :--- | :--- | :--- | :--- | :--- | | 1 | [DeepSeek](https://openaitools.app/tool/deepseek) | 372,761,591 | +4.3% | Freemium | High-efficiency open-weight and hosted reasoning AI powered by DeepSeek-V4 and R1 at ultra-low token costs. | [.md](https://openaitools.app/tool/deepseek.md) | | 2 | [Ollama](https://openaitools.app/tool/ollama) | 9,421,973 | +4.3% | Free, Freemium, Paid | Run and build with open AI models locally or in the cloud | [.md](https://openaitools.app/tool/ollama.md) | | 3 | [Hermes-Agent](https://openaitools.app/tool/hermes-agent-1) | 3,638,741 | -5.4% | Freemium | A free, open-source personal AI agent that learns your projects and works across every messaging app. | [.md](https://openaitools.app/tool/hermes-agent-1.md) | | 4 | [Clawdbot (OpenClaw)](https://openaitools.app/tool/clawdbot) | 1,903,068 | -25.4% | Paid, Free | Open-source personal AI assistant that runs on your own machine and works from chat apps | [.md](https://openaitools.app/tool/clawdbot.md) | | 5 | [Dify](https://openaitools.app/tool/dify-ai) | 819,691 | -15% | Freemium | Build, deploy, and govern production-ready agentic AI workflows | [.md](https://openaitools.app/tool/dify-ai.md) | | 6 | [Open WebUI](https://openaitools.app/tool/open-webui) | 804,534 | -2.8% | Freemium | Self-hosted AI workspace for local and cloud models | [.md](https://openaitools.app/tool/open-webui.md) | | 7 | [AnythingLLM](https://openaitools.app/tool/anythingllm) | 512,269 | +12.9% | Freemium | Own your intelligence: a private AI assistant that runs entirely on your machine | [.md](https://openaitools.app/tool/anythingllm.md) | | 8 | [LlamaIndex](https://openaitools.app/tool/llamaindex) | 387,910 | +19.9% | Freemium, Contact for Pricing | Turn any document into AI-ready context with agentic parsing and extraction | [.md](https://openaitools.app/tool/llamaindex.md) | | 9 | [LibreChat](https://openaitools.app/tool/librechat) | 346,879 | -3.5% | Free | Unites multiple AI providers in a customizable open-source platform, but is typically deployed as a web server. | [.md](https://openaitools.app/tool/librechat.md) | | 10 | [Dyad](https://openaitools.app/tool/dyad) | 207,981 | +3.5% | Freemium | Free, open-source AI app builder that runs on your own machine | [.md](https://openaitools.app/tool/dyad.md) | --- Source: https://openaitools.app/compare/jan-vs-rlama More Markdown: [Categories](https://openaitools.app/categories.md) · [Traffic rankings](https://openaitools.app/rankings.md) · [llms.txt](https://openaitools.app/llms.txt)