jellycell 1.1.1

A living catalogue for reproducible analyses: plain-text notebooks, content-hashed output caching, live HTML viewer.

jellycell

A living catalogue for reproducible analyses. Plain-text notebooks, content-addressed cell-output caching, live HTML viewer. Agent-friendly from day one.

Why jellycell

Jupyter notebooks are great for exploration and awful for everything after. They diff poorly, store outputs alongside code, make provenance opaque, and have no story for "which step produced this file?" — so analyses drift, results become unreproducible, and the notebook gets rewritten as a script anyway.

jellycell keeps the things that made notebooks useful (cell-level thinking, rich outputs, narrative + code interleaved) and fixes what made them painful:

• Source is plain Python. Notebooks are .py files in jupytext percent format with a PEP-723 dependency header. Diffable, greppable, uv run-able. No JSON-wrapped base64.
• Outputs are content-addressed. Every cell's output is keyed on (normalized source, declared + detected deps, lockfile-aware env hash). Re-run → cache hit. Change a cell → only that cell + its dependents re-execute. Outputs live in a sidecar cache, not in your commits.
• Provenance is first-class. Every artifact is tagged with the cell + notebook + cache key that produced it. jc.load("artifacts/foo.json") implicitly declares the dep edge. No hand-written DAGs.
• HTML reports are free. jellycell render produces byte-identical static HTML; jellycell view serves the same pages live with SSE-backed reload while you edit. Side-nav, artifact browser, no build step.
• Agents drop in without onboarding. Every CLI command supports --json with a versioned schema. jellycell prompt --write drops AGENTS.md + CLAUDE.md at your repo root so Cursor, Codex, GitHub Copilot, Claude Code, Aider, and every other AGENTS.md-native tool picks up the canonical agent guide with zero config.

Install

pip install jellycell            # CLI only (no live viewer)
pip install 'jellycell[server]'  # with `jellycell view`

Requires Python ≥ 3.11.

Quickstart

jellycell init my-project
cd my-project

# (Once per repo) drop AGENTS.md + CLAUDE.md so Cursor / Codex / Copilot / Claude Code read jellycell's agent guide
jellycell prompt --write

# Scaffold a notebook
jellycell new tour
# edit notebooks/tour.py in your editor of choice

# Run it — first time executes, subsequent runs hit the cache
jellycell run notebooks/tour.py

# Build static HTML reports
jellycell render notebooks/tour.py

# Or serve the live viewer with reload on save (requires [server] extra)
jellycell view

Inside a cell, the jc.* helpers work transparently:

# %% cell-id=tour:1 name=raw
import pandas as pd
df = pd.read_csv("data/sales.csv")
jc.save(df, "artifacts/sales.parquet")

# %% cell-id=tour:2 name=summary deps=raw
df = jc.load("artifacts/sales.parquet")
summary = df.describe()
jc.table(summary)

Change tour:1 → tour:2 invalidates on the next run. Edit nothing → both hit the cache.

Full docs: https://jellycell.readthedocs.io

Composition, not reinvention

jellycell's value is the composition, not the parts. We piggyback on the best tools in the Jupyter ecosystem:

We piggyback on	For
jupytext	.py ↔ notebook IR
jupyter-client	kernel subprocess + messaging
nbformat	.ipynb round-trip
diskcache	content-addressed blob store
nbconvert (output helpers)	mime bundle → safe HTML
markdown-it-py + MyST plugins	markdown rendering
watchfiles + sse-starlette	live reload
pydantic + typer	CLI + config + schemas

The bits we own are the ones that tie these together: tag vocabulary, cache key derivation, manifest format, per-cell orchestration, page shell, dep graph, agent guide. The full piggyback map is in docs/reference/architecture.md.

Is this for you?

Probably yes if:

• You write analyses in notebooks and wish they diffed.
• You keep rerunning a 10-minute cell to regenerate one plot.
• You need reviewers or agents to understand what produced what.
• You want a single project-wide HTML report, not a directory of disconnected .ipynb files.

Probably no if:

• You want a fully-featured cloud notebook platform.
• You need real-time collaboration / multiplayer editing.
• Your workflow is tightly coupled to JupyterLab extensions.

Project structure

my-project/
├── jellycell.toml          # project config
├── notebooks/              # .py notebooks (jupytext percent format)
├── data/                   # inputs
├── artifacts/              # jc.save outputs
├── site/                # rendered HTML (jellycell render)
└── .jellycell/cache/       # content-addressed cache (gitignored)

Documentation

• Tutorial: getting-started
• Notebook format: file-format
• Project layout: project-layout
• CLI reference: cli-reference
• Agent guide: agent-guide — what jellycell prompt emits
• Reference: docs/reference/ — living architecture + §10 contracts
• Historical spec: docs/spec/v0.md — frozen genesis / v1.0 build spec

Contributing

See CONTRIBUTING.md and docs/development/. Short version: bugfix PRs are welcome and bump patch; feature PRs should open an issue first. The three §10 contracts (--json schemas, cache key algorithm, agent guide content) are deliberate ceremonies — read docs/development/releasing.md before touching them.

License

Apache-2.0. See LICENSE.