AnythingLLM
AnythingLLM is a self-hosted application for chatting with your documents, complete out of the box: a web interface, workspaces that scope which documents a conversation can see, user management, and connectors for a wide choice of model providers and vector stores. It is the quickest route in this library to putting a working internal knowledge assistant in front of real users.
View the repo · Mintplex-Labs/anything-llm ↗Teams that want a usable internal assistant without assembling one from parts, and leaders who want to prove value on real documents before funding a bespoke build. It suits pilots with a defined user group and document set. Teams needing deep customisation of retrieval behaviour, fine-grained per-document permissions tied to an identity provider, or an assistant embedded inside another product will outgrow it, and should plan for that from the start.
Install itexport STORAGE_LOCATION=$HOME/anythingllm && mkdir -p $STORAGE_LOCATION
docker run -d -p 3001:3001 --cap-add SYS_ADMIN -v $STORAGE_LOCATION:/app/server/storage -e STORAGE_DIR=/app/server/storage mintplexlabs/anythingllmTreat the defaults as a starting point: choose your embedder, vector store and model provider deliberately, since the out-of-the-box choices favour convenience over your specific accuracy needs. Access is scoped at the workspace level rather than per document, so map that against your confidentiality requirements before loading anything sensitive. Put it behind a reverse proxy with proper authentication, and measure answer accuracy on a test set before the whole company starts trusting it.
Where Blash AI comes inWe often use AnythingLLM to prove the case in weeks, then harden what works: tighter permissions, citations, an evaluation set, and model routing through LiteLLM. When the pilot exposes needs the application cannot meet, we rebuild the retrieval layer on Chroma with the lessons already learned, so nothing spent on the pilot is wasted.
When you want this running on your real stack, that is the engagement
Book an AI audit →n8n →
n8n is workflow automation you run on your own infrastructure. It connects the applications a business already uses through a visual editor, with hundreds of prebuilt nodes, a code step for anything bespoke, and native support for calling language models mid-flow. Think of it as a self-hosted alternative to Zapier or Make that you own outright, including every piece of data passing through it.
Ollama →
Ollama runs open-weight language models on your own machine or server. One command downloads a model and serves it behind a local API that most existing AI tooling can talk to, so an application built against a hosted provider can point at your own hardware instead. Sensitive data stays inside your network, there are no per-token fees, and it keeps working with no internet connection at all.
LiteLLM →
LiteLLM is a gateway that lets you call models from Anthropic, OpenAI, Google and many other providers through one consistent API. It runs as a Python library inside your code or as a standalone proxy server in front of your whole stack, with cost tracking, per-key budgets, rate limits and automatic fallbacks for when a provider has a bad day.