Jaroslav Beneš 1d3f6c450b Users, groups, permissions, model settings and reasoning display
Four features, plus the schema machinery they needed.

**Schema sync.** The first live instance had data in it, and create_all
only creates missing *tables* -- a new column silently never appeared.
db/migrations.py now diffs the declared models against the database and
ALTER TABLE ... ADD COLUMN for what is missing, deriving a backfill
default from the column type (SQLite refuses a NOT NULL column without
one, and a Python-side `default=dict` cannot be expressed in DDL).
Verified against a copy of the live database: eight changes applied, all
rows preserved, second run a no-op. Renames, drops and retypes are still
manual and say so.

**Permissions.** A flat set of named booleans: an instance baseline
widened by each group the user belongs to. A group grants and never
denies -- with denies, "why can this user not do X" cannot be answered
without simulating every group. Admins bypass entirely, because an admin
can grant it back to themselves in two clicks and pretending otherwise
is theatre. Model *access* is separate: public, or granted to groups.
The picker is not the boundary -- switching a chat to a model you cannot
reach is a 403.

**Model settings.** Ordering, pinned-first, an instance default and a
per-user default, display names, descriptions, capability flags, and
uploaded images. Images are stored and served locally rather than by
URL: a remote URL makes every page render a request to a third party.
Uploads are validated by magic number, not the declared content type,
and stored under a random name. Models with no image get a generated
initial whose hue is derived from the model id, so it is stable.

**Reasoning display.** Streams into its own collapsible block above the
answer, labelled "Thought for 14 seconds", collapsed once finished, and
never replayed as context on the next turn. Two sources: the
reasoning_content delta field, and <think> tags inline in content -- the
latter needs a streaming splitter because the tags arrive split across
chunks. Models emitting no reasoning show nothing, via a :has() rule
rather than JavaScript. Verified against qwen35-9b on llama-swap: 694
reasoning events, 52 answer tokens, cleanly separated.

Two bugs found and fixed while testing:

- A bare `Mapped[list]` relationship is treated by SQLAlchemy as a scalar
  and returns None instead of []. It needs the element type.
- FastAPI substitutes the default for an empty form value, so with
  `x: str | None = Form(None)` a submitted `x=` is indistinguishable from
  an absent field. That silently broke clearing a system prompt or a
  temperature. update_chat now reads the raw form and checks key presence.

143 tests, ruff clean.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
2026-07-21 11:49:32 +02:00
2026-07-21 08:02:48 +00:00

LLeMbas — waybread for the long road of thought

A self-hosted web UI for your language models, written in Python.
Talks to anything that speaks the OpenAI API. Themed after Middle-earth.

Python 3.11+ License GPL-3.0 No Node required


Lembas is the Elvish waybread — one bite sustains a traveller for a day's march. The capitals hide what it runs on: LLeMbas.

Why this exists

Most self-hosted LLM front-ends are large JavaScript applications with a Python API bolted underneath. LLeMbas is the other way round: server-rendered Python, with htmx and a little Alpine for interactivity. There is no package.json, no bundler, no build step, and nothing is fetched from a CDN at runtime. Clone it, pip install -e ., run it.

Features

Working now

  • Chats — streaming replies, Markdown with server-side syntax highlighting, copy and regenerate, automatic chat titles, per-chat system prompt and sampling settings
  • Reasoning display — thinking from reasoning models streams into its own collapsible block, labelled with how long it took, and is never replayed as context
  • Folders — arbitrarily nested, delete a folder without losing the chats inside it
  • OpenAI connections — point at OpenAI, LM Studio, vLLM, llama.cpp, llama-swap, Ollama or OpenRouter; models are discovered and cached
  • Model settings — ordering, pinned models, an instance default and a per-user default, custom names and descriptions, uploaded model images
  • Users, groups & permissions — per-group grants that union rather than override, and model access restricted to chosen groups
  • Accounts — first account becomes the administrator, argon2 password hashing, revocable server-side sessions, self-service password change, admin-managed accounts
  • Admin settings — open or close registration from the UI, stored in the database and effective immediately
  • Two themesMoria (dark) and Shire (light), switchable per user

Planned

File upload, vision and PDFs · built-in tools with admin settings · custom tools and MCP servers · agentic execution (local and over SSH) · image generation.

Quick start

git clone https://git.houmeres.sk/Houmeres/LLeMbas.git
cd LLeMbas

python -m venv .venv && . .venv/bin/activate
pip install -e ".[dev]"

cp .env.example .env
lembas secret-key           # paste the result into LEMBAS_SECRET_KEY

lembas serve                # http://127.0.0.1:8080

Open the address and create the first account — it becomes the administrator. Then go to Admin → Connections and add an endpoint. For a local runner that is usually http://localhost:1234/v1 with no API key. Press Test & refresh and its models appear in the chat model picker.

The vendored browser libraries (htmx, Alpine) are committed, so no network access is needed to run. To re-fetch or bump them: python scripts/fetch_vendor.py --update.

Configuration

All variables are prefixed LEMBAS_ and can live in .env. See .env.example for the annotated list.

Variable Default Purpose
LEMBAS_SECRET_KEY generated Signs sessions and encrypts stored API keys. Set this. A generated key changes every restart, signing everyone out and making stored API keys unreadable.
LEMBAS_DATA_DIR ./data SQLite database and uploads.
LEMBAS_HOST / LEMBAS_PORT 127.0.0.1 / 8080 Bind address.
LEMBAS_ALLOW_SIGNUP true Whether new users may register themselves — the initial value only. Once set under Admin → General the stored setting wins. The first account is always an admin regardless.
LEMBAS_DEFAULT_THEME moria moria (dark) or shire (light).
LEMBAS_SESSION_TTL 2592000 Session lifetime in seconds.
LEMBAS_REQUEST_TIMEOUT 300 Seconds to wait on an upstream model.

Commands

lembas serve          # run the server
lembas info           # where data lives, what is configured
lembas secret-key     # generate a value for LEMBAS_SECRET_KEY
lembas create-admin   # create or promote an administrator

How it fits together

Browser  ──form POST──▶  FastAPI  ──▶  SQLite
   ▲                        │
   │                        └──httpx──▶  any OpenAI-compatible endpoint
   └──── server-sent events ◀───────────────┘   (streamed reply)

Sending a message stores the turn and returns two HTML fragments: the user's bubble and an empty assistant bubble carrying an sse-connect. That opens a server-sent event stream which appends tokens as they arrive, then replaces the whole bubble with the finished, Markdown-rendered version. Rendering and highlighting happen in Python, so the streamed and final views cannot disagree.

src/lembas/
  api/         routes: auth, chats, folders, admin, pages
  db/models/   SQLAlchemy schema
  security/    password hashing, sessions
  services/    llm client, chat orchestration, markdown, crypto, sse
  web/         Jinja templates and static assets
assets/        SVG artwork masters
scripts/       artwork generator, vendored-JS fetcher
deploy/        systemd unit and nginx vhost for a real install

Development

pytest                              # test suite
ruff check .                        # lint
python scripts/build_artwork.py     # regenerate the SVG artwork
python scripts/fetch_vendor.py      # verify vendored JS against the lockfile

There is no Alembic. The schema is SQLite-only and synchronised at startup: missing tables and missing columns are added automatically, so adding a field to a model needs nothing but a restart. Renames, drops and retypes are still manual — see CLAUDE.md.

Artwork

The logo, favicon and banner are original vector work, generated by scripts/build_artwork.py so the mallorn leaf stays identical across every size it appears at. The wordmark is Source Serif 4 (SIL OFL 1.1) converted to outlines — a README banner cannot load a webfont, and <text> would render in whatever serif the reader happens to have.

Licence

GPL-3.0.

A note on the theme

This is an independent hobby project, themed as an affectionate nod to J.R.R. Tolkien's world. It is not affiliated with, endorsed by, or connected to the Tolkien Estate, Middle-earth Enterprises, or any related rights holder. All artwork here is original.

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