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Private AI for courses, on your own hardware

Your class, answered.

A course assistant that answers from your readings and lists its sources, on your school's own servers.

See how it worksSecurity overviewGitHub

Runs with

  • Ollama
  • LM Studio
  • LocalAI
  • KoboldCPP
  • Docker
  • Kubernetes
  • Helm
  • OpenShift

Who it's for

Built for classrooms. Ready for teams.

A student's question about carrying capacity in a course workspace, answered from the readings, with each reading listed under the answer.

Ask questions about the course. Answers drawn from its readings list the sources they used.

See how sources work

Build a course workspace from your syllabus and readings, add your students, and see how they use it.

Set up a course in four steps

Make a workspace for any subject, add your own notes and readings, and keep each topic in its own thread.

See install options

Run it on school servers with local models and no internet, or set a different provider for each workspace.

Read the security overview

Companies get the same workspaces and answers with sources, and agents that use the tools admins turn on.

Compare editions

Grounded answers

Answers that show their readings.

When a course workspace has documents, Mission LLM searches them before the model answers. Students open Sources to see which readings an answer used, the passages it pulled and how closely each one matched.

  • Sources listed under grounded answers
  • Retrieved passages with match scores
  • Query mode, with a refusal message you write

How your data flows

A student's answer about energy flow and the nitrogen cycle, with the Sources panel open: each course reading it drew on, a passage from it and its match score.

Security and control

Your school sets the boundary.

The Privacy & Data-Handling page with the anonymous telemetry switch off, and the vectors kept in the built-in LanceDB store on the instance.

Set DISABLE_TELEMETRY="true" before the first start and no usage events are sent. Or turn it off in Settings.

Learn more about telemetry

Pair it with a local model runtime and stage the built-in models. Chat, embeddings and search then run offline.

Learn more about offline use

Instructors get the manager role, which opens every workspace. Students see only the ones they are added to.

Learn more about access

Admins and managers can read every workspace chat, see who sent it, and export it as CSV, JSON or JSONL.

Learn more about oversight

Sign-ins, failed sign-ins, and changes to users, API keys, invites, workspaces and documents, in one log for admins.

Learn more about oversight

Ready for class in four steps.

  1. Install on your server

    One container serves the app and API on port 3001, with all course files and chats in one storage folder.

  2. Connect a local model

    Point it at Ollama, LM Studio or another runtime on your campus network. Cloud providers work too.

  3. Add your readings

    Create a workspace for each course and drop in its files. Mission LLM indexes them on your server.

  4. Invite your students

    Turn on multi-user mode, invite students, and add each one to the workspaces for their courses.

Self-hosted

Run it on a lab workstation or a campus server.

Mission LLM ships as one container that serves the app and API on port 3001. Start it with Docker, Compose, Kubernetes, Helm or OpenShift, on your own hardware or in a cloud account you control.

See every install option

Docker on Linux or macOS
export STORAGE_LOCATION=$HOME/missionllm && \
mkdir -p $STORAGE_LOCATION && \
touch "$STORAGE_LOCATION/.env" && \
docker run -d --rm -p 3001:3001 \
--cap-add SYS_ADMIN \
-v ${STORAGE_LOCATION}:/app/server/storage \
-v ${STORAGE_LOCATION}/.env:/app/server/.env \
-e STORAGE_DIR="/app/server/storage" \
REGISTRY/mission-llm:latest
REGISTRY is a placeholder. Public images are not published yet. Today you build the image from source (see Download).

Resources

MIT licensed foundation.

Open at the core. Yours to run.

Mission LLM is built on the open-source AnythingLLM project (MIT License) and adds an in-place upgrade, a documented list of outbound connections and deployment templates that pull only your image.

License
MIT
User roles
3
Cloud templates
3

See the AnythingLLM project

What Mission LLM adds, on top of the AnythingLLM core

  1. In-place upgrade from AnythingLLM

    The database, settings and vector collections carry over.

  2. Documented outbound connections

    Every connection the server can make, and how to avoid it.

  3. Templates that pull only your image

    Deployment templates run the image you build and host.

AnythingLLM core (MIT License)

  • Ingestion
  • Retrieval
  • Agents
  • Providers

Bring it to your next course.

Start with one container, a local model and one course. Questions first? Tell us what you teach or run and we will reply by email.

Talk to us