High-performance Python SDK for the vterm-rs PTY orchestrator
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# vterm-rs [](https://crates.io/crates/vterm-rs) [](https://pypi.org/project/vterm-rs-python-mcp/) [](https://github.com/margusmartsepp/vterm-rs/actions) [<img src="https://cursor.com/deeplink/mcp-install-dark.svg" alt="Install in Cursor" height="20">](https://cursor.com/en/install-mcp?name=vterm-rs&config=eyJjb21tYW5kIjoidXZ4IiwiaW5zdGFsbCI6InZ0ZXJtLXJzLXB5dGhvbi1tY3AiLCJhcmdzIjpbInZ0ZXJtLXJzLXB5dGhvbi1tY3AiXX0%3D) > **The High-Performance Rust PTY Orchestrator for AI Agents.** `vterm-rs` is a state-aware terminal host built specifically for AI agents (and the humans who build them). It transforms the terminal from a "blind black box" into a **State Machine** that agents can inspect, reason about, and control fluently. ## Why vterm-rs? * **Safety (Guardrails)**: Prevent "Infinite Log Floods" with `max_lines` and `max_duration` limits. * **Truth (State Machine)**: Don't guess if a command finished. Use `wait_until` and `screen_read` to inspect the visual grid. * **Fluent Fleet**: Orchestrate multiple terminals atomically with the high-performance `batch()` API. * **Headless-First**: Designed for CI/CD and AI backends, with optional `--visible` mode for debugging. ## Quick Start (Python SDK) ```python import vterm_python client = vterm_python.VTermClient() # The "Fluent Fleet" way: Atomically set up your session ops = [ client.spawn_op("Build", visible=True), client.write_op(1, "cargo build<Enter>"), client.wait_until_op(1, "Finished", timeout_ms=30000) ] result = client.batch(ops) print(f"Build status: {result['sub_results'][2]['status']}") ``` ```mermaid graph LR Agent["AI agent<br/>(Claude/IDE/MCP)"] Term["vterm.exe<br/>(PTY pool, vt100, reaper)"] Shell["powershell.exe<br/>(your real shell)"] Agent -- "NDJSON o…
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