tithon 0.1.1

Tithon - The immortal Python REPL that will set you free.

Attention!

Currently Tithon is alpha version. It may has lots of bugs and issues.
Please report issues when you find any bugs which can help me to develop Tithon.

Tithon

The immortal Python REPL that will set you free. Tithon keeps an interactive Python (Jupyter) kernel running on a remote host independently of any client, and losslessly restores cell output, progress, and widget state whenever a client (re)connects — close your laptop mid-run, reopen over an SSH/VSCode tunnel hours later, and your outputs are still there and still streaming.

The name is from Tithonus of Greek myth, granted immortality but not eternal youth — like a remote kernel that stays alive while its outputs wither the moment the client disconnects. Tithon lifts the curse: immortality, with eternal youth this time.

The authoritative design document is docs/SPEC.md; it also covers current implementation maturity and the verification matrix.

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Features

• Kernel persistence. The kernel is spawned detached (setsid) and is not a child of the daemon, so it survives daemon restarts, crashes, and disconnects.
• Loss-free journal. Every iopub/shell message is preserved verbatim in a SQLite (WAL) journal, plus a per-execution folded snapshot (the current display state) for fast reconnects.
• Snapshot + delta sync. Clients attach with a last_seen_seq and receive a snapshot followed by a monotonic-sequence delta stream — gapless and ordered.
• Live streaming with bounded cost. Output streams to cells as it runs; rendering is coalesced so a 50,000-iteration loop collapses to a handful of UI updates instead of melting the renderer.
• Outputs live in the journal, not the file. A percent-format .py stays pure source (byte-exact round-trip, clean diffs); outputs are reattached to cells by content hash. Rich outputs (images) are stored as files, not base64.
• Widget state mirror. ipywidgets comm traffic is folded into a widget-state+json snapshot — a tqdm bar with 50k updates reconnects as a single bar, not 50k events.
• Host protection. A slow or stalled client cannot grow daemon memory without bound or block other clients; the daemon caps per-subscriber buffers and drops clients that fall too far behind (they reconnect and resync).
• Unix-socket only. The daemon binds a 0600 unix domain socket — no TCP.

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How it works

TODO: Show program architecture image.

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Requirements

• Python 3.11+ (the daemon and CLI).
• etc..?

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Installation

1. Tithon CLI

Using pip

pip install tithon

Using uv

uv add tithon

2. VSCode extension

From VS Code Marketplace

1. Open VS Code
2. Go to Extensions
3. Search for "tithon"
4. Click "Install"

Or install directly from the VS Code Marketplace

Command Line:

code --install-extension rnoro.tithon

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Quickstart(CLI)

State (socket, log, journal, artifacts) lives under TITHON_HOME, default ~/.tithon.

1. Start the daemon (it runs in the foreground; background it):

tithon daemon &
tail -f ~/.tithon/daemon.log     # optional

2. Run code — kernel state persists across calls:

tithon run -c 'x = 41'
tithon run -c 'x += 1; print(x)'     # -> 42
tithon status

3. Survive a disconnect — the kernel outlives the daemon, and a reconnect restores everything:

tithon run -c 'for i in range(3): print("line", i)'

pkill -9 -f 'tithon daemon'          # kill the daemon; the kernel lives on
tithon daemon &                      # restart -> re-attaches the same kernel

tithon attach --since 0 --once       # full snapshot: the prior output is back
tithon run -c 'print(x)'             # -> 42, kernel state intact

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Quickstart(Remote workflow)

This is the workflow Tithon is built for: you edit from a laptop while the kernel runs on a remote GPU host and keeps running across your disconnects. With a VSCode Tunnel (or Remote-SSH) connection the VSCode Extension Host runs on the remote host, so the Tithon extension reaches the daemon's host-local unix socket directly — no port forwarding. Your laptop only renders the UI, so closing it never stops the work.

From your remote client:

1. Connect to the host (VSCode ▸ Connect to Tunnel… or Remote-SSH) and open your project folder.
2. Open a percent-format .py. Run cells with the Run Cell CodeLens, then run Tithon: Start Live Output Sync to stream output into the cells as it is produced.
3. Close the laptop or drop the connection — the daemon and kernel keep running on the host.
4. Reconnect later and run Tithon: Restore Cell Outputs from Daemon (or just reopen the notebook): the outputs are back and resume streaming live.

Note. The daemon and the extension must share TITHON_HOME (both default to ~/.tithon for the same user on the host). Because the Extension Host runs on the host, it uses the host-local socket — no manual forwarding. If you instead run the extension on your laptop against a remote daemon, you must forward the unix socket yourself (e.g. SSH RemoteForward or socat); that is not the default path.

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CLI reference

Command	Description
tithon daemon	Run the daemon (foreground). Owns the kernel and serves clients.
tithon run -c CODE	Submit code and stream its output. --no-wait prints the exec id and exits; --timeout N bounds the wait.
tithon attach	Stream events as NDJSON. --since N sets the resume point; --once exits after the backlog sync; --until-done exits after the next completion.
tithon status	Print session, queue, kernel, and widget-model status.

attach --since is the reconnect knob:

• --since 0 — full folded snapshot, then live delta.
• --since N — replay only events after seq N, then a sync marker, then live.
• --since -1 — live only (ignore history).

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VSCode extension

The extension opens percent-format .py files as a notebook (tithon-py) and talks to the daemon over its unix socket. Commands:

Command	What it does
Tithon: Restore Cell Outputs from Daemon (tithon.restoreOutputs)	Reconnect and restore the journal's folded outputs into the notebook cells.
Tithon: Start Live Output Sync (tithon.startLive)	Keep a session open and stream output into cells in real time.
Run Cell (CodeLens, tithon.runCell)	Submit a # %% cell's code to the daemon.

Outputs are matched to cells by content hash, so they survive edits and reopens; an output whose cell was edited since it ran is flagged stale.

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Configuration

Environment variables read by the daemon and CLI:

Variable	Default	Purpose
TITHON_HOME	~/.tithon	Root for the socket, log, journal, and artifacts.
TITHON_SUB_QUEUE_MAX	10000	Max queued events per client before it is dropped (backpressure).
TITHON_SEND_TIMEOUT	10.0	Seconds a client may stall a send before being dropped.
TITHON_WRITE_BUFFER_HIGH	1048576	Per-connection send-buffer high-water mark (bounds daemon memory).
TITHON_SOCK_SNDBUF	1048576	Per-connection kernel socket send buffer.
TITHON_SUB_POLL	0.5	Interval at which a blocked sender re-checks for drop.

Where outputs are stored. A percent .py never holds outputs. They live in $TITHON_HOME/sessions/default/journal.db (raw messages + folded snapshots), with rich outputs (images) written as files under <workdir>/.tithon/outputs/ and referenced from the journal.

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Architecture invariants

These are load-bearing; see docs/SPEC.md for the full rationale.

1. The kernel is spawned detached and its connection file is persisted, so the daemon re-attaches to it after a restart.
2. All iopub/shell messages are kept verbatim in SQLite (WAL), with a folded per-execution snapshot maintained alongside.
3. Client sync is snapshot + delta over a monotonically increasing sequence.
4. Rich image/* outputs are stored as files, not base64 in the journal.
5. The daemon binds a 0600 unix domain socket only — never TCP.

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