A conversational assistant is really two things stacked together: a chat interface, and the model that answers behind it. ChatGPT fuses them and hosts both, which is why every prompt travels to a vendor's servers, a hard stop the moment a conversation touches customer records, source code, or anything under a confidentiality clause, and why you are tied to whichever models that vendor chooses to price and keep running. The open source alternatives below are chat interfaces you run yourself, pointed at whatever model you like: a local one on your own hardware, or an API key you control. The conversation stays on infrastructure you operate, and changing the model underneath becomes a setting rather than a migration.
Ollama gets you up and running with open large language models on your own machine. Pull a model from its library and chat with it in a single command, keeping inference and your data on hardware you control rather than a hosted service.
Open WebUI is an extensible, feature-rich, self-hosted AI platform designed to run entirely offline. It connects to Ollama and OpenAI-compatible APIs, giving a single interface for chatting with local or remote models while keeping everything on hardware you control.
LobeHub is an AI agent workspace for finding, building, and collaborating with agent teammates. It treats agents as the unit of work, organizing them into round-the-clock operation so you can hand off tasks instead of staying online.
GPT4All runs large language models privately on everyday desktops and laptops. Download the app and start chatting in minutes, with no API calls or GPUs required, so your prompts and the model's responses never leave your machine.
AnythingLLM is an all-in-one AI app for building a private, ChatGPT-style workspace. Connect a local or cloud LLM, ingest your documents, and start chatting in minutes, with everything running locally by default and no configuration required to get going.
Jan is an open-source ChatGPT replacement that runs AI chat on your own computer. Download and run open models locally with full control and privacy, keeping conversations on your machine unless you choose otherwise.
LibreChat is a self-hosted AI chat platform that unifies conversations with many AI providers in one interface. It is built for people who want a single place for chat, file-based work, and provider switching without moving between separate apps.
Sort by whether you are one person on a laptop or a team behind a shared server.
For a self-hosted team platform: Open WebUI Open WebUI runs entirely offline as a self-hosted platform, connecting to Ollama and any OpenAI-compatible API. It carries local RAG across nine vector databases, role-based access control with granular permissions, and voice and video chat, and installs with Docker or Kubernetes. A responsive mobile PWA adds offline access on phones.
For unifying many model providers: LibreChat LibreChat brings OpenAI, Anthropic, AWS Bedrock, Google, and custom OpenAI-compatible endpoints into one interface, with agents, MCP tools, web search, and a sandboxed code interpreter. Multi-user authentication supports OAuth2, LDAP, and email login, and it deploys with Docker, fitting teams that want provider choice without switching apps.
For a local desktop assistant: Jan Jan runs open models on your own computer, pulling from HuggingFace or connecting out to OpenAI, Anthropic, Mistral, and others when you choose. It exposes an OpenAI-compatible server at localhost so other apps can use it, and supports MCP for agentic workflows. Native builds cover Windows, macOS, and Linux.
For running locally without a GPU: GPT4All GPT4All runs large language models on everyday desktops and laptops with no API calls and no GPU required, so prompts and responses never leave the machine. LocalDocs points the chat at your own files for grounded, fully local answers, and native installers cover Windows, macOS, and Ubuntu.
A useful way to sort these tools is by who they are for: one person on a laptop, or a team behind a shared server. For a single user who wants prompts to never leave the machine, Jan and GPT4All are desktop apps that run open models locally, with GPT4All needing no GPU and working CPU-only, while Jan pulls models from HuggingFace and also exposes an OpenAI-compatible server for other apps. For a team, Open WebUI and LibreChat run as self-hosted servers with multi-user authentication, role-based access, and a shared history.
The next decision is what you want beyond plain chat. If you mainly want to ask questions of your own documents, AnythingLLM and Open WebUI include retrieval built in: AnythingLLM ships document pipelines with source citations, and Open WebUI carries local RAG across nine vector databases. If you connect to many providers, LibreChat unifies OpenAI, Anthropic, Google, and custom OpenAI-compatible endpoints in one interface with agents and a code interpreter. If you want agents that run on a schedule, LobeChat organizes them into groups and timed runs. Match the extras to what your users actually reach for.
Moving off ChatGPT is a workflow exercise more than a data import. Use the account export to retrieve conversation history, but treat it as an archive, since few tools import that structure directly. Saved prompts copy cleanly into system prompts, templates, or workspace instructions, while custom assistants need rebuilding because their uploaded knowledge files, actions, and permissions rarely transfer. The privacy payoff only holds if the whole path stays yours, so watch external model APIs, telemetry, web-search plugins, and logging, and treat the chat UI, inference server, and vector store as one boundary.
Sometimes, but not reliably across every task. Open source models can be very capable for coding help, rewriting, summarizing, extraction, and domain-specific workflows. ChatGPT may still feel stronger on broad instruction following, multimodal tasks, tool use, and consistency, because the model, interface, and infrastructure are tuned together. Test with your own prompts and real edge cases rather than generic benchmarks before you decide.
Not always. Smaller models run on a laptop or CPU-only server, and GPT4All is built specifically to need no GPU, though response speed and answer quality are limited. Larger models usually want a modern GPU or a rented accelerator to feel interactive. For a team, the practical question is concurrency: one person testing prompts is very different from dozens sending long requests all day.
It can keep prompts, files, and chat logs inside your own environment, but only if the whole path stays there. Watch for external model APIs, telemetry, web-search plugins, document parsers, and logging defaults that quietly reach out. Treat the chat UI, inference server, vector database, and storage bucket as one security boundary rather than separate harmless pieces, and confirm whether admins can read conversations before sensitive data goes in.
Yes. Most of these interfaces are model-agnostic: Jan, LibreChat, Open WebUI, LobeChat, and AnythingLLM can all point at hosted providers through an API key as well as local models. That lets you keep a strong cloud model for general work while routing sensitive conversations to a local one, and switching between them stays a setting change rather than a move to a different application.
Use ChatGPT's account data export where it is available. The export is mainly an archive and a source of prompts, not a guaranteed import format for another tool, so expect to search through JSON or HTML and copy important threads by hand. Saved prompts move easily into a new tool's system prompts or templates, but workflows that relied on uploaded files or tools need rebuilding.
Maybe, but these are not automatic just because the chat layer is open source. File upload needs parsing, storage, retrieval, and prompt assembly, which tools like AnythingLLM and Open WebUI provide through built-in RAG. Images require a model that accepts visual input, and voice needs speech-to-text and text-to-speech components. Each added mode increases deployment complexity, latency, and the scope of your security review.
Expect code changes even when an alternative offers a similar chat-completion shape. Authentication, streaming behavior, tool calling, error formats, token counting, context limits, and rate limits can all differ. A tool like Jan exposes an OpenAI-compatible local server that eases the swap, but re-run your evaluations on real prompts and edge cases, and test embeddings, function calling, and structured JSON output separately rather than assuming drop-in compatibility.
