Web servicesLocal & self-hosted appsMintplex LabsOpen source
AnythingLLM
Decision matrix
AnythingLLM app decision matrix
Overview
Verdict: the fastest way to put a folder of documents behind a chat window with citations, as long as you accept that team logins mean running a server yourself. The free desktop app covers one person; move to Docker only when someone else needs access too.
Other good fits
A small team that already knows which model and vector database it wants and can run a Docker container for it.
+ Pros
Read pros
AnythingLLM turns a folder of documents into a chat workspace: ask a question and get an answer with a citation back to the source passage. The desktop app runs entirely on one machine, free, with no account. The same open-source project also ships as a Docker container, which adds logins, user management and a browser interface you can put behind your own domain for a team.
Pick it when…
You want to ask questions across PDFs, meeting notes or a wiki export and check which document an answer came from.
You already have a model in mind, local (through Ollama or LM Studio) or hosted (with your own API key), and want a workspace around it instead of a command line.
A small team needs one shared instance with logins, not five separate desktop installs.
− Cons
Read cons
A citation shows you which document an answer drew from, not that the model read it correctly; check the passage yourself before you rely on the answer. The same flexibility that lets you pick any model or vector database also means your documents can end up at a third-party provider the moment you connect one, so check that choice before you upload anything sensitive.
Pick something else when…
You want a plain chat front end for a local model, without workspaces or citations.Open WebUI is lighter for that job.
You need a visual workflow builder around the model, not just a chat window.Dify adds branching logic, tools and a published app.
You want to wire retrieval into a longer agent chain.Flowise gives you a node editor for that.
Avoid if
You need a guarantee that documents never reach a third-party model; that depends entirely on which provider you connect.
Workflow
Install the desktop app, or run the Docker image on a server → choose a language model, an embedding model and a vector database → create a workspace and add your documents → ask a question and check the citation against the source → widen the workspace only once the first answers hold up.
multi-user logins, a browser interface, your own domain
Cloud Basic
$50/month
Managed private instance, bring your own model API key
Cloud Pro
$99/month
Higher performance, 72-hour support response
Cloud Enterprise
Custom
On-premise option, SSO, role-based access
A Docker instance needs a small VPS or a spare machine, plus you keeping it updated and backed up. Whatever model you point it at bills separately, per token, on top of any AnythingLLM plan.
Restrictions
The desktop build has the full developer API (reachable only from your own computer unless you switch on network discovery) but no multi-user login; logins and user management require the Docker route. Anything you connect, a hosted model or an external vector database, can receive the content of your documents, so the actual data path depends on your choices, not on AnythingLLM itself. Self-hosting puts updates, backups and access control on you.
First project
Documentation-based suggestion — not run here.
Build a two-document workspace with a colleague's meeting notes and a project brief, both with the names changed. Ask for the deadlines and owners named in them, then open the citation and confirm it points at the right passage. Keep it to made-up or already-public material until you've read the privacy page for the model and vector database you picked.
Done when every answer's citation actually contains the fact quoted, and you've corrected at least one answer that mixed up the two documents.
Links & evidence
Documentation-based · not hands-on · Sep 23, 2026
Rests on AnythingLLM's pricing page, its documentation site, the Docker quickstart and the privacy and data handling page, all read on 2026-09-23. No AnythingLLM instance was installed or run for this review.
Chat with PDFs and folders using citations in a familiar workspace.
Choose it when local models should ground answers in your documents.
The Docker build adds a shared endpoint and OpenAI-compatible API.
Mintplex Labs · free · cloud · self-host
Container image: mintplexlabs/anythingllm:latest
private rag
document chat
pdf indexing
self-hosted api
docker deployment
local model
citation support
team endpoint
search-cite
talk-think
draft-docs
data-analytics
View sourced imageStill frame from the product demonstration linked in the official AnythingLLM README.
Mintplex-Labs/anything-llm
MIT-licensed project documentation, official README-linked product demonstration. Copyright and permission notice retained; a single GIF frame is extracted without cropping.
The MIT License
Copyright (c) Mintplex Labs Inc.
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.
