argcomplete-snapshot 0.1.0

Fast Bash completion artifacts for import-heavy argparse and argcomplete CLIs.

argcomplete-snapshot

What

Generate fast Bash completion artifacts for import-heavy Python CLIs built with argparse and argcomplete.

Why

Default argcomplete runs the target Python CLI on every completion request. For SDK-backed CLIs that means paying for:

• Python startup
• heavy imports
• parser construction
• completion logic

That can easily turn <TAB> into a 200-600+ ms operation.

How

argcomplete-snapshot extracts a lightweight completion model once and writes shell-native cache artifacts:

• snapshot-v1.json
• completion-v1.bash
• version-stamp

Steady-state completion stays in Bash. Python only runs when:

• artifacts are missing
• the CLI changed after upgrade
• a maintainer explicitly routes a dynamic resolver through a fallback command

Why Not Just Use Something Else?

• argcomplete: good dynamic behavior, but slow because it starts Python on every completion
• shtab: fast for static completion scripts, but not built around lazy self-refresh after pip install -U ... or explicit dynamic fallback contracts

This package is for the middle ground:

• your CLI is too heavy for default argcomplete
• you still need more lifecycle support than a pure static script

Requirements

• Python 3.13+
• Bash 4+

Bash 4+ is required because the generated runtime uses associative arrays.

Dynamic resolvers require an explicit fallback command. They are not inferred automatically.

Install

Install from PyPI:

pip install argcomplete-snapshot

Install benchmark extras from a checkout:

pip install ".[bench]"

Usage

1. Build a lightweight parser

import argparse

from argcomplete_snapshot import CompletionKind, set_completion


def build_parser() -> argparse.ArgumentParser:
    parser = argparse.ArgumentParser(prog="mycli")
    parser.add_argument("--output", choices=["json", "yaml", "text"])

    profile = parser.add_argument("--profile")
    set_completion(profile, resolver="config_profiles")

    path = parser.add_argument("--path")
    set_completion(path, kind=CompletionKind.FILE)

    return parser

2. Generate artifacts

acs-refresh refresh \
  --factory mypackage.cli:build_parser \
  --cli-name mycli \
  --distribution mycli

3. Print the activation snippet

acs-refresh print-activation \
  --factory mypackage.cli:build_parser \
  --cli-name mycli \
  --distribution mycli

4. Add dynamic fallback only when needed

acs-refresh print-activation \
  --factory mypackage.cli:build_parser \
  --cli-name mycli \
  --distribution mycli \
  --fallback-command "mycli --resolver-fallback"

Use fallback only for genuinely dynamic completions. Static options and choices should stay on the Bash hot path.

Cache Layout

~/.cache/argcomplete-snapshot/<cli-name>/
  snapshot-v1.json
  completion-v1.bash
  version-stamp

Benchmarks

Results:

• benchmarks/results/README.md
• benchmarks/results/benchmark-results.json
• benchmarks/results/hot-path-comparison.png
• benchmarks/results/spawned-comparison.png

Hot path:

Spawned shell:

Current published medians:

Case	argcomplete	snapshot	shtab
Static hot path	491.10 ms	1.77 ms	23.46 ms
Static spawned shell	647.65 ms	165.43 ms	95.86 ms

Read those numbers carefully:

• hot path is the relevant steady-state completion metric
• spawned shell includes shell startup and sourcing overhead
• dynamic fallback cases are slower because they intentionally call runtime logic

Benchmark environment for the published results:

• macOS 26.3.1
• MacBook Pro MacBookPro17,1
• 8 CPU cores (4 performance + 4 efficiency)
• 16 GB memory
• Python 3.13.12 under uv
• benchmark fixtures simulate heavy CLIs by importing requests and sleeping for 200 ms
• benchmark strategy uses 5 spawned-shell samples and 15 hot-path samples per case and implementation

Run locally:

make benchmark

Development

pip install -e ".[dev,bench]"
make check
make benchmark

Status

Early release.

Implemented:

• typed snapshot model
• argparse extraction
• Bash artifact emission
• lazy cache refresh
• activation snippet generation
• explicit dynamic fallback contract
• benchmark fixtures and comparisons