This review sits beside two others on the site. model-hosting-platforms-comparison-review covers model hosting platforms such as Hugging Face, ModelScope, and Replicate; this piece covers self-hosted AI assistant and chat products. agent-credential-permission-comparison-review covers Agent credential and permission management; this piece takes the selection view. The three are complementary.
Scope, Method, and How This Review Fits
This piece compares form and ownership only, not model capability. We walk six dimensions: deployment shape, multi-user capability, data ownership, open-source license, model access, and IM or client channels. Let us pin the scope down first.
First, this review runs no benchmark and draws no conclusion about which product is smarter. The five sit in front of different models, so inference quality, context length, and tool calling are outside scope. If you came for "who answers best", this is the wrong article.
Second, every star count is a GitHub API snapshot from 2026-09-17. Stars roll constantly, so treat these as that day's picture and check the live count yourself. The language and license fields come from the same response; NOASSERTION means a custom LICENSE file ships with the repo, not that there is no license.
Third, for any custom license the exact terms are governed by the repository LICENSE file. This review paraphrases no clause it has not verified and makes no legal judgment about commercial use for you. Before adopting any of these in a company, read the LICENSE text yourself.
The Five at a Glance
Every figure below comes from the GitHub API on 2026-09-17. Stars are that day's snapshot, and the license column is the raw API field.
| Project | Repository | Stars (2026-09-17 snapshot) | Language | License (API field) |
|---|---|---|---|---|
| Octop | TencentCloud/Octop | 3,198 | Python | MIT |
| Open WebUI | open-webui/open-webui | 152,349 | Python | NOASSERTION (custom) |
| Dify | langgenius/dify | 156,096 | TypeScript | NOASSERTION (custom) |
| FastGPT | labring/FastGPT | 29,680 | TypeScript | NOASSERTION (custom) |
| LibreChat | danny-avila/LibreChat | 44,350 | TypeScript | MIT |
Dify and Open WebUI lead in the hundred-fifty-thousand-star range. LibreChat sits near forty-four thousand and FastGPT near thirty thousand, a second tier. Octop, just over three thousand, is the youngest yet most distinctive in positioning.
From the repository descriptions and general field knowledge, the five anchor on different mental models. Open WebUI is a self-hosted chat frontend with its own interface that runs against Ollama or the OpenAI API; it is an entry point where a person or small team opens a browser and talks. LibreChat is a similar multi-vendor chat frontend and is MIT licensed, so its positioning sits closest to Open WebUI; the difference is mostly engineering taste and defaults.
Dify is an agentic workflow plus RAG application platform whose keyword is "build applications": you produce a publishable app or workflow, not just a conversation. FastGPT is a knowledge-base QA platform whose keyword is "knowledge base", with strength in retrieval augmentation and document QA.
Octop is a multi-user, multi-agent assistant platform and the only name here that treats multi-user isolation as a first-class goal: each family or team member gets independent memory and expert config, it deploys as a single process, its IM channels reach Feishu, DingTalk, QQ, Discord, WeCom, and Telegram, all data lands in ~/.octop/, and models are self-configured via OpenAI-compatible endpoints, Ollama, or nearly twenty providers.
One caveat: this review does not mention LobeChat. Its official repository is currently unreachable, so the data cannot be verified; mentioning it would mean inventing numbers, so we leave it out.
Positioning Tiers and Multi-User Isolation
The first step in selection is to decide whether you want a chat tool or an application. That decision tells you which half of the list to read.
The chat-frontend group is Open WebUI and LibreChat; their deliverable is a conversational interface where you plug in a local or API model, and the value is interaction polish and multi-model switching. The application-platform group is Dify and FastGPT; their deliverable is AI capability other people can use, Dify leaning to workflow orchestration and agents, FastGPT to knowledge-base retrieval. The assistant-platform group, here only Octop, delivers long-running assistants configured per person.
The operations mindset differs across the three: a chat frontend is a tool, an application platform is a product, an assistant platform is a team. Mixing them is the common mistake, like installing Open WebUI for a knowledge base or picking Dify for personal chat.
The multi-user dimension is where this comparison gets sharpest. In a family or team, independent memory per person is Octop's unique point: under one instance, Zhang's history and config never leak into Li's because isolation lives at the account layer. The other four are mostly shared workspaces that default to one user or share one context, so strict isolation usually means rebuilding the architecture or adding an external identity system.
Why does isolation matter so much in a family? Because a shared machine naturally mixes ages and roles: a child's homework help, an elder's health questions, and an adult's work drafts in one instance with shared context lay everyone's privacy on one table. Octop isolates at the account layer, so each person logs in and sees only their own. The others can do this too, but they treat privacy as an extra goal to bolt on, and the cost is architectural, not cosmetic.
This is not to say the other four are bad; their primary target is simply not family sharing. Open WebUI and LibreChat have user systems, but their focus is multiple people collaborating in one frontend, not people invisible to each other in one instance. If you want the latter, Octop is the only one here that satisfies it natively.
Deployment Shape and Operations Cost
Deployment shape decides whether your weekend becomes an on-call shift. Octop deploys as a single process, defaults to SQLite, and can switch to PostgreSQL; one command brings it up with data fixed at ~/.octop/. This shape suits individuals and small teams who avoid Docker Compose and keeps the operations surface smallest.
The other four mostly start with Docker Compose. Dify and FastGPT carry more components, each often in its own container, so the first deploy takes care. Open WebUI and LibreChat are lighter but still usually run in containers in production.
Operations cost shows up as how many logs you read when something breaks: one process versus first finding which box is throwing a tantrum. Single process helps a strapped solo operator; for a team on Kubernetes, Compose is the natural unit.
Resource footprint matters too. A single process keeps resident memory controlled and runs on a small two-core four-gigabyte machine, fitting a spare laptop or cheap host. Multi-container deployments keep several processes resident with the database and vector store in memory year-round, so the starting configuration is higher. For a solo operator this is real money.
One more note on upgrades. A single-process product upgrades by stopping the old process and running the new version, with migration handled by the program. A multi-container product upgrade means watching version compatibility, especially schema changes in the vector store and database. Neither is superior in the abstract; each only fits your infrastructure.
Model Access and Data Ownership
Model access splits in two. One is bring-your-own: you paste your API key or local endpoint and the platform binds to no vendor. Octop takes this path with OpenAI-compatible interfaces, Ollama, and nearly twenty providers; Open WebUI and LibreChat are likewise mostly bring-your-own.
The other is platform-built-in with own-model binding. Dify and FastGPT integrate recommended model services and workflow nodes deeply, which eases app building but pulls some of your choices toward platform defaults; the deeper you go, the higher the migration cost.
Local-model privacy belongs here too. Running a local model through Ollama keeps the prompt and answer on the machine, a hard requirement for compliance-sensitive cases. The bring-your-own school (Octop, Open WebUI, LibreChat) hands you that whole chain: where the model runs and where data goes is your call. The platform-built-in school is more convenient, but its default chain may include the platform's recommended cloud model, so check the call path before relying on it.
Data ownership is the common denominator: all five are self-hosted with no default flow back to a vendor. The difference is granularity. Octop lands at the local-directory level, one folder you back up by copying; Dify and FastGPT keep data at the server level across a database and vector store, backed up by export. The former suits individuals; the latter suits enterprises.
If you care intensely that not one byte of your chat log leaves this machine, the single-process plus local-directory shape lets you sleep best. If you care that team data is compliant, retained, and traceable, the database-level shape may suit you more. Self-hosting solves the ownership question; it does not solve the management posture that ownership leaves behind.
Dimension Matrix and Selection Guide
Let us spread the six dimensions into one comparison table, then give advice by audience. In the license column, NOASSERTION is uniformly read as "custom license; commercial and redistribution terms are governed by the repository LICENSE file", and this review interprets no specific clause for you.
| Dimension | Octop | Open WebUI | LibreChat | Dify | FastGPT |
|---|---|---|---|---|---|
| Deployment | Single process (SQLite/PG) | Mostly Docker | Mostly Docker | Docker Compose | Docker Compose |
| Multi-user | Account-level hard isolation | Shared workspace | Shared workspace | Workspace-level | Workspace-level |
| Data ownership | Local dir ~/.octop/ | Self-hosted server | Self-hosted server | Self-hosted server | Self-hosted server |
| License | MIT | Custom | MIT | Custom | Custom |
| Model access | Bring-your-own, ~20 | Mostly bring-your-own | Mostly bring-your-own | Built-in plus BYO | Built-in plus BYO |
| IM channels | Feishu/DingTalk/QQ/WeCom/Discord/Telegram | Limited | Limited | Limited | Limited |
On the license dimension: Octop and LibreChat are MIT, so redistribution is free with few constraints. Open WebUI, Dify, and FastGPT report NOASSERTION, meaning a custom LICENSE file ships, and its brand, commercial, and redistribution constraints must be read from the source before any company adopts them.
Advice by audience:
| Audience | Suggested pick | One-line reason |
|---|---|---|
| Solo local trial | Open WebUI or LibreChat | Open browser and chat; lightest deploy; try both MIT and custom |
| Family sharing | Octop | Only native account-level isolation; per-person memory stays separate |
| Small team collaboration | Octop or LibreChat | Want isolation, pick Octop; want shared frontend collaboration, pick LibreChat |
| Build a knowledge-base app | FastGPT | Retrieval augmentation and document QA land most smoothly |
| Orchestrate workflows | Dify | Agentic workflow canvas is the home ground; output is a publishable app |
To close: the conclusion is not "who is strongest" but "who most resembles the thing you actually want". Get form and ownership clear first, then talk about models. To see how Octop lands as a single process, refer to octop-deploy-sop; to keep comparing at the model-hosting layer, see model-hosting-platforms-comparison-review; for Agent credential and permission concerns, see agent-credential-permission-comparison-review.
FAQ
Q1: What does this review actually compare, and what does it leave out?
A1: It compares only form and ownership across six dimensions: deployment shape, multi-user capability, data ownership, open-source license, model access, and IM channels. It does not compare model inference quality, runs no benchmark, and draws no conclusion about which is smarter.
Q2: Why do the star counts differ from what I see now?
A2: Every star count is a GitHub API snapshot from 2026-09-17, and stars move constantly, which is normal. Trust the live number when you open the repository; the figures here represent only that day.
Q3: Can the custom-license projects be used commercially?
A3: For any project whose API field reports NOASSERTION (Open WebUI, Dify, FastGPT), this review states uniformly that it is a custom license and commercial plus redistribution terms are governed by the repository LICENSE file. Whether commercial use is allowed, and what brand or redistribution constraints apply, is for you to read from the LICENSE source; this review makes no legal judgment for you.
Q4: For a household sharing one machine, which one isolates without re-architecting?
A4: Only Octop on this list does native account-level hard isolation, so each family member's memory and expert config stay separate. The others are mostly shared-workspace shapes, and strict isolation usually means wiring in an external identity system or changing the architecture.
Q5: I just want to chat locally with data never leaving this machine, which do I pick?
A5: For the lightest deploy, pick Open WebUI or LibreChat; for data locked entirely in a local directory plus IM channels, pick Octop, whose data all lands in ~/.octop/ and backup is just copying the directory.