@beihaizb/dsh-notebook

English | 中文

A stateful, Agent-controllable Jupyter workspace for DeepSeek Harness. A persistent ipykernel runtime that your DSH agent can read, edit, execute, and inspect — turning a notebook from a static artifact into an operationable target.

This is not just another Notebook frontend. The kernel is truly persistent (variables live across cells), the notebook saves and reloads, and the agent has tools that read, modify, run, and reason about cells and their outputs. That combination gives the agent a real Agent ↔ Kernel ↔ Artifact loop.

Agent workflow

The point of this plugin is that the DSH agent doesn't run one-shot scripts — it drives a live computational session:

User: "Switch Harmony integration to scVI." → Agent finds the relevant cell → reads its source and context → edits the cell → executes it → checks stdout / traceback → iterates if needed.

Because the kernel is persistent, the agent is operating on stateful runtime state (an Anndata, a GPU model, loaded data), not just assembling strings.

Screenshots / Demo

• Live tqdm progress bars and inline figures with click-to-zoom.
• Clickable traceback frames (Cell In[N]) that jump to the offending cell.
• Per-cell "hand to AI" revision box: type a request, the agent edits and reruns that cell.
• VS Code-aligned cell behavior (queued / executing states, execution semantics).

Features (implemented)

• Real persistent kernel: ipykernel + jupyter_client sidecar — variables persist across cells.
• Agent tools to read, edit, run, and inspect cells (nb_get, nb_edit_cell, nb_run_cell, ...) with structured access to outputs and tracebacks.
• Runtime introspection for agents: nb_context, nb_list_vars, and nb_inspect_object expose live kernel variables and compact notebook state; pandas DataFrame objects get shape / dtype / missing-value / head / summary metadata.
• Safe AI edit loop: rich per-cell version snapshots, nb_cell_history, nb_revert_cell, nb_error_context, and nb_edit_and_run_cell support auditable repair-and-rerun workflows.
• VS Code-aligned cell UI: circular run control, Queued / Executing status bar, execution-number glyph.
• VS Code-aligned execution semantics: Restart keeps completed outputs, Interrupt stops only the current cell, Run All stops on error, plus Clear Outputs and Restart & Clear.
• tqdm progress bars, long-output folding, multi-image grid.
• Error navigation (click traceback to jump to the cell).
• Resilient frontend: a React error boundary keeps the notebook view alive instead of going blank when an output fails to render (fixed a hooks-order crash that could blank the panel).
• Faster live updates: live progress is throttled and compacted (last outputs only), state polling is reduced, and frequent updates no longer resend the full kernel list.
• Session-aware working directory: the notebook default cwd/workspace follows the current DSH session; manual "set working directory" and opened-notebook cwd take priority.
• Per-session isolation: each DSH session owns an independent notebook, kernel, and working directory — no cross-session state leakage. Multiple sessions can run kernels simultaneously.
• Kernel manager: a toolbar popup lists all active kernels across sessions (session ID, kernel name, status); close any session's kernel directly from the manager.
• Kernel activity indicator: a status bar shows whether the current session's kernel is active and warns when multiple sessions have kernels running (memory / resource awareness).
• Parent watchdog: if dsh web is force-killed, the Python sidecar detects parent-process death and auto-shuts-down its ipykernel — no orphan processes (cross-platform: getppid on POSIX, OpenProcess on Windows).
• Bilingual UI (EN / 中文): the entire interface defaults to English with a one-click language toggle in the toolbar; your choice persists across sessions.
• Hybrid-CPU hint: on big.LITTLE (P+E core) machines a one-time, per-session hint explains how to pin the Python process to performance cores (Windows Task Manager / Activity Monitor / taskset), with a copy-ready psutil snippet; dismiss it and it won't repeat.
• Jedi kernel completion (df. / plt. / variable names; Tab to accept; hover for docstrings).
• Per-cell AI revision (version history in cell.metadata.dsh).
• Standard .ipynb save / load with autosave and unsaved-changes warning.
• Kernel picker over conda environments, with a friendly install hint when ipykernel is missing.

Roadmap

The trajectory is toward a full agent computational workspace, not more Notebook UI:

• AnnData / scientific object introspection — extend nb_inspect_object("adata") with n_obs, layers, obsm, and obs / var column summaries.
• Structured execution results — return cell_id, execution_count, stdout, stderr, display_data, error, duration, kernel_state to the agent for a reliable execution loop.
• Execution history / diff UI — expose auditable cell versions in the browser, not just in tools / cell.metadata.dsh.
• Context-aware cell selection — layer / query which cells define or depend on a variable, instead of stuffing the whole notebook into context.
• Execution safety — classify read-only / lightweight / mutating / expensive / destructive operations; confirm before destructive or very long runs.
• Checkpoint / rollback — recover not just code but runtime state.
• Remote / SLURM kernel — run the kernel server-side / on a job while the agent drives it through the same interface.

Install

dsh plugin --profile web add @beihaizb/dsh-notebook

Then restart dsh web. A Notebook tab appears at the top of the session.

Kernel selection

• dsh-envsel is an optional dependency. With the environment picker installed, the plugin reads its selection (~/.dsh/envsel-state.json) and uses the conda environment you picked for the session as the default kernel. Install:

  dsh plugin --profile web add @beihaizb/dsh-envsel
  

  (Source: github.com/beihzb/dsh-envsel)

• Without dsh-envsel, the plugin falls back to the first discovered conda environment that has ipykernel installed.

• You are never locked in. Switch kernels from the toolbar dropdown at any time.

The selected environment needs ipykernel, jupyter_client, and nbformat. If any is missing, the plugin shows a friendly error with the exact install command.

Tools

nb_new / nb_open / nb_save / nb_get / nb_context / nb_list / nb_list_vars / nb_inspect_object / nb_add_cell / nb_delete_cell / nb_move_cell / nb_edit_cell / nb_edit_and_run_cell / nb_cell_history / nb_revert_cell / nb_error_context / nb_run_cell / nb_run_all / nb_apply_suggestion / nb_kernel_restart / nb_kernel_restart_and_clear / nb_kernel_interrupt / nb_kernel_list / nb_kernel_select / nb_clear_outputs / nb_set_cwd

License

MIT