Thales Maciel
2de31306b6
Rewrite the user-facing persistent sandbox story around pyro workspace ..., including the install guide, first-run transcript, integrations notes, and public contract reference. Rename the Python example to examples/python_workspace.py and update the docs to use the new workspace create, sync, exec, status, logs, and delete flows with seed_path/workspace_id terminology. Mark the 2.4.0 workspace-contract pivot as done in the roadmap now that the shipped CLI, SDK, MCP, docs, and tests all use the workspace-first surface.
116 lines
3.3 KiB
Markdown
116 lines
3.3 KiB
Markdown
# 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](install.md) or [first-run.md](first-run.md).
|
|
|
|
## Recommended Default
|
|
|
|
Use `vm_run` first for one-shot commands.
|
|
|
|
That keeps the model-facing contract small:
|
|
|
|
- one tool
|
|
- one command
|
|
- one ephemeral VM
|
|
- automatic cleanup
|
|
|
|
Move to `workspace_*` only when the agent truly needs repeated commands in one workspace 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`
|
|
- `workspace_create(seed_path=...)` + `workspace_sync_push` + `workspace_exec` when the agent needs persistent workspace state
|
|
|
|
Canonical example:
|
|
|
|
- [examples/openai_responses_vm_run.py](../examples/openai_responses_vm_run.py)
|
|
|
|
## 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:
|
|
|
|
- [examples/mcp_client_config.md](../examples/mcp_client_config.md)
|
|
- [examples/claude_desktop_mcp_config.json](../examples/claude_desktop_mcp_config.json)
|
|
- [examples/cursor_mcp_config.json](../examples/cursor_mcp_config.json)
|
|
|
|
## 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(...)`
|
|
- `Pyro.create_workspace(seed_path=...)` + `Pyro.push_workspace_sync(...)` + `Pyro.exec_workspace(...)` when repeated workspace commands are required
|
|
|
|
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
|
|
- use `create_workspace(seed_path=...)` when the agent needs repeated commands in one persistent
|
|
`/workspace` that starts from host content
|
|
- use `push_workspace_sync(...)` when later host-side changes need to be imported into that
|
|
running workspace without recreating it
|
|
|
|
Examples:
|
|
|
|
- [examples/python_run.py](../examples/python_run.py)
|
|
- [examples/python_lifecycle.py](../examples/python_lifecycle.py)
|
|
- [examples/python_workspace.py](../examples/python_workspace.py)
|
|
|
|
## 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 one-shot framework tool input directly onto `vm_run`
|
|
- expose `workspace_*` only when the framework truly needs repeated commands in one workspace
|
|
|
|
Concrete example:
|
|
|
|
- [examples/langchain_vm_run.py](../examples/langchain_vm_run.py)
|
|
|
|
## 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.
|