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pyro-mcp/docs/integrations.md
Thales Maciel 6e16e74fd5 Harden default environment pull behavior 
Fix the default one-shot install path so empty bundled profile directories no longer win over OCI-backed environment pulls or leave broken cached symlinks behind.

Treat cached installs as valid only when the manifest and boot artifacts are all present, repair invalid installs on the next pull, and add human-mode phase markers for env pull and run without changing JSON output.

Align the Python lifecycle example and public docs with the current exec_vm/vm_exec auto-clean semantics, and validate the slice with focused pytest coverage, make check, make dist-check, and a real default-path pull/inspect/run smoke.
2026-03-11 19:27:09 -03:00

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Integration Targets

These are the main ways to integrate pyro-mcp into an LLM application.

Use this page after you have already validated the host and guest execution through the CLI path in install.md or first-run.md.

Recommended Default

Use vm_run first.

That keeps the model-facing contract small:

  • one tool
  • one command
  • one ephemeral VM
  • automatic cleanup

Only move to lifecycle tools when the agent truly needs VM state across multiple calls.

OpenAI Responses API

Best when:

  • your agent already uses OpenAI models directly
  • you want a normal tool-calling loop instead of MCP transport
  • you want the smallest amount of integration code

Recommended surface:

  • vm_run

Canonical example:

MCP Clients

Best when:

  • your host application already supports MCP
  • you want pyro to run as an external stdio server
  • you want tool schemas to be discovered directly from the server

Recommended entrypoint:

  • pyro mcp serve

Starter config:

Direct Python SDK

Best when:

  • your application owns orchestration itself
  • you do not need MCP transport
  • you want direct access to Pyro

Recommended default:

  • Pyro.run_in_vm(...)

Lifecycle note:

  • Pyro.exec_vm(...) runs one command and auto-cleans the VM afterward
  • use create_vm(...) + start_vm(...) only when you need pre-exec inspection or status before that final exec

Examples:

Agent Framework Wrappers

Examples:

  • LangChain tools
  • PydanticAI tools
  • custom in-house orchestration layers

Best when:

  • you already have an application framework that expects a Python callable tool
  • you want to wrap vm_run behind framework-specific abstractions

Recommended pattern:

  • keep the framework wrapper thin
  • map framework tool input directly onto vm_run
  • avoid exposing lifecycle tools unless the framework truly needs them

Concrete example:

Selection Rule

Choose the narrowest integration that matches the host environment:

  1. OpenAI Responses API if you want a direct provider tool loop.
  2. MCP if your host already speaks MCP.
  3. Python SDK if you own orchestration and do not need transport.
  4. Framework wrappers only as thin adapters over the same vm_run contract.