pybun-cli 0.1.10

PyBun shim bootstrapper for signed releases

PyBun (Python Bundle)

🐍 The Agent-First Python Runtime 🤖

pip + venv + test runner + MCP server — all in one Rust binary.
Built for AI agents (JSON-first) and humans alike.

Quick Start • Why PyBun? • MCP Server • Commands • Roadmap

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Quick Start

macOS / Linux:

curl -LsSf https://raw.githubusercontent.com/VOID-TECHNOLOGY-INC/PyBun/main/scripts/install.sh | sh

Windows (PowerShell):

irm https://raw.githubusercontent.com/VOID-TECHNOLOGY-INC/PyBun/main/scripts/install.ps1 | iex

Or via pip/pipx (PyPI):

pipx install pybun-cli
# or
pip install pybun-cli

Then run:

pybun add requests
pybun run -c -- "import requests; print('Hello, PyBun!')"

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Why PyBun?

Existing Python tools are built for humans. PyBun is designed for both AI Agents and humans.

Feature	Traditional Tools (pip, uv, Poetry)	PyBun
Output Format	Human-readable text	🤖 JSON-first (--format=json)
AI Integration	Manual parsing required	🔌 Built-in MCP Server
Error Handling	Unstructured error messages	📋 Structured diagnostics with hints
Agent Automation	Fragile text scraping	✅ Reliable machine-readable output

✨ Key Differentiators

• 🤖 AI Native: Every command supports --format=json as a first-class citizen. LLMs can parse outputs reliably without fragile regex.
• 🔌 MCP Server Built-in: MCP (Model Context Protocol) lets AI tools like Cursor and Claude Desktop operate your Python environment directly—no extra setup.
• ⚡ Rust Speed: Blazingly fast dependency resolution and installation.
• 🛡️ Sandbox Mode: Run untrusted AI-generated code safely with --sandbox.
• 📦 Single Binary: No dependencies. Just download and run.

💡 Example: AI Agent Workflow

# AI agent asks: "Install pandas and show the version"
$ pybun --format=json add pandas
{"status": "ok", "detail": {"added": ["pandas==2.2.0"], ...}}

$ pybun --format=json run -c -- "import pandas; print(pandas.__version__)"
{"status": "ok", "stdout": "2.2.0\n", ...}

The AI receives structured JSON—no regex parsing needed.

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Status

• Current: M1 (Fast Installer), M2 (Runtime Optimization), and M4 (MCP/JSON) are in progress (stable/preview/stub mixed)
• Platforms: macOS/Linux (arm64/amd64), Windows (preview)

For feature maturity (stub/preview/stable) and phased rollout policy, see docs/SPECS.md.

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Installation

The easiest way to install PyBun:

pip install pybun-cli

Other installation methods

macOS / Linux (shell script):

curl -LsSf https://raw.githubusercontent.com/VOID-TECHNOLOGY-INC/PyBun/main/scripts/install.sh | sh

Windows (PowerShell):

irm https://raw.githubusercontent.com/VOID-TECHNOLOGY-INC/PyBun/main/scripts/install.ps1 | iex

From source:

cargo install --path .

Note: If your PATH resolves pybun to Bun, use pybun-cli instead.

Command Reference

Package Management

# Install dependencies (generates lockfile)
pybun install --require requests==2.31.0 --index fixtures/index.json

# Add a package (updates pyproject.toml)
pybun add requests

# Remove a package
pybun remove requests

Script Execution

# Run a Python script
pybun run script.py

# Run with arguments
pybun run script.py -- arg1 arg2

# Run inline code
pybun run -c -- "import sys; print(sys.version)"

# Run with profile
pybun run --profile=prod script.py

PEP 723 inline metadata is also supported:

# /// script
# requires-python = ">=3.11"
# dependencies = ["requests>=2.28"]
# ///
import requests

※ Currently, metadata parsing and display are the main features (preview), with auto-install and isolated environment execution planned for phased rollout (see docs/PLAN.md for details).

Ad-hoc Execution (pybun x)

Install a package in a temporary environment and execute it (Python version of npx):

# Temporarily install and run cowsay
pybun x cowsay

# Specify version
pybun x cowsay==6.1

# With arguments
pybun x black -- --check .

Python Version Management

# Show installed versions
pybun python list

# Show all available versions
pybun python list --all

# Install Python
pybun python install 3.12

# Remove Python
pybun python remove 3.12

# Show Python path
pybun python which
pybun python which 3.11

Runtime Optimization

Module Finder

Rust-based high-speed module search:

# Find a module
pybun module-find os.path

# Scan a directory for all modules
pybun module-find --scan -p ./src

# With benchmark
pybun module-find --benchmark os.path

Lazy Import

# Show configuration
pybun lazy-import --show-config

# Check module decision
pybun lazy-import --check numpy

# Generate Python code
pybun lazy-import --generate -o lazy_setup.py

# Specify allow/deny lists
pybun lazy-import --allow mymodule --deny debug_tools --generate

File Watch (Development Mode)

# Watch for file changes and re-run (currently preview)
# Native watching is planned for phased rollout. For now, use --shell-command (external watcher).
pybun watch main.py

# Watch a specific directory
pybun watch main.py -p src

# Show configuration
pybun watch --show-config

# Generate shell command for external watcher
pybun watch --shell-command main.py

Profile Management

# Show available profiles
pybun profile --list

# Show profile settings
pybun profile dev --show

# Compare profiles
pybun profile dev --compare prod

# Export profile
pybun profile prod -o prod-config.toml

Profiles:

• dev: Hot reload enabled, verbose logging
• prod: Lazy imports enabled, optimizations
• benchmark: Tracing and timing measurement

MCP Server

MCP server for AI agents:

# Start in stdio mode
pybun mcp serve --stdio

Tools: pybun_resolve, pybun_install, pybun_run, pybun_gc, pybun_doctor
Resources: pybun://cache/info, pybun://env/info

※ Currently pybun_gc, pybun_doctor, pybun_run, pybun_resolve, and resources are operational. pybun_install generates lockfiles via resolution. HTTP mode is not yet implemented.

Diagnostics & Maintenance

# Environment diagnostics
pybun doctor
pybun doctor --verbose

# Cache garbage collection
pybun gc
pybun gc --max-size 1G
pybun gc --dry-run

# Self-update check
pybun self update --dry-run
pybun self update --channel nightly

Sandbox usage

Use the sandbox for untrusted scripts or PEP 723 snippets:

pybun --format=json run --sandbox examples/hello.py
pybun --format=json run --sandbox --allow-network -c "print('net ok')"

The sandbox isolates file and network access; add --allow-network only when required. Combine with --profile=prod for production-like runs.

Profiles

Profiles tune defaults for performance vs. development ergonomics:

• dev (default): hot reload enabled, verbose logging.
• prod: lazy imports and optimizations enabled, quieter output.
• benchmark: stable timing/logging for reproducible benchmarks.

Examples:

pybun profile --list
pybun run --profile=prod app.py
pybun test --profile=benchmark --format=json

MCP server (stdio)

Operate PyBun as an MCP server for agents/IDEs:

pybun mcp serve --stdio
pybun --format=json mcp serve --stdio  # JSON envelope for tooling

Tools: pybun_resolve, pybun_install, pybun_run, pybun_gc, pybun_doctor. Resources: pybun://cache/info, pybun://env/info. HTTP mode remains TODO; stdio is the GA path.

JSON output examples

All commands support the --format=json option (schema v1). Examples:

pybun --format=json run -c -- "print('hello')"

{
  "version": "1",
  "command": "pybun run",
  "status": "ok",
  "detail": {
    "summary": "executed inline code"
  },
  "events": [],
  "diagnostics": []
}

Failure example:

pybun --format=json run missing.py

{
  "version": "1",
  "command": "pybun run",
  "status": "error",
  "diagnostics": [
    {
      "kind": "runtime",
      "message": "missing.py not found",
      "hint": "pass -c for inline code or a valid path"
    }
  ]
}

Tests/builds emit structured summaries (pass/fail counts, shard info) while keeping the same envelope:

pybun --format=json test --fail-fast
pybun --format=json build

Enable trace IDs for debugging:

PYBUN_TRACE=1 pybun --format=json run script.py

Environment Variables

Variable	Description
PYBUN_ENV	Path to venv to use
PYBUN_PYTHON	Path to Python binary
PYBUN_PROFILE	Default profile (dev/prod/benchmark)
PYBUN_TRACE	Set to 1 to enable trace ID
PYBUN_LOG	Log level (debug/info/warn/error)

Release note automation

• Generate GA release notes from tags:
  python scripts/release/generate_release_notes.py --repo . --previous-tag v0.1.0 --tag v0.2.0 --notes-output release/RELEASE_NOTES.md --changelog CHANGELOG.md
• Attach the notes to the release manifest (served by installers/self-update via release_notes in JSON):
  python scripts/release/generate_manifest.py --assets-dir release --version 0.2.0 --channel stable --base-url https://github.com/VOID-TECHNOLOGY-INC/PyBun/releases/download/v0.2.0 --output pybun-release.json --release-notes release/RELEASE_NOTES.md
• CI-friendly JSON summary: python scripts/release/generate_release_notes.py --repo . --previous-tag v0.1.0 --tag v0.2.0 --format json

Upgrade guide

See docs/UPGRADE.md for pre-GA → GA migration notes, breaking changes, and the recommended CI checks (doc lint/link + release note automation).

Development

Requirements

• Rust stable (rustup, cargo)

Basic Commands

# Format
cargo fmt

# Lint
cargo clippy --all-targets --all-features -- -D warnings

# Test
cargo test

# Development scripts
./scripts/dev fmt
./scripts/dev lint
./scripts/dev test

Testing

# All tests
cargo test

# Specific tests
cargo test cli_smoke
cargo test json_schema
cargo test mcp

Roadmap

• M0: Repository & CI scaffold
• M1: Fast installer (lockfile, resolver, PEP 723)
• M2: Runtime optimization (module finder, lazy import, hot reload)
• M3: Test runner (discovery, parallel execution, snapshots)
• M4: JSON/MCP & diagnostics
• M5: Builder & security
• M6: Remote cache, workspaces

See docs/PLAN.md for details.

Privacy & Telemetry

PyBun does not collect telemetry by default (opt-in model).

# Check telemetry status
pybun telemetry status

# Enable telemetry
pybun telemetry enable

# Disable telemetry
pybun telemetry disable

Collected data (when enabled):

• Command usage (anonymized)
• Error diagnostics
• Performance metrics

Never collected: API keys, tokens, credentials, passwords, or file contents.

Environment override: PYBUN_TELEMETRY=0|1

License

MIT