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An owner without developer skills can export the agent and restore it working on a runtime or host the provider does not operate, within minutes of asking and without provider approval. Running the runtime on the owner's own machine counts as not provider-operated.

Desktop has an in-app Export and Import pair for sessions (JSON), and the CLI has `goose session export`; both work at once on the owner's machine. Restoring the rest of the agent (config.yaml, permission.yaml, memory files, prompt overrides, skills, schedules, keyring secrets) means copying files and re-entering keys by hand, and there is no documented procedure for it. That is one shortfall. Sources

Export is immediate (the agent is one .adf file; copying it and using Open is documented), but restoring a working agent on another machine takes several documented steps: import the owner seed phrase to unlock sealed keys, re-add a model provider key (app-level keys are not in the file), re-register app-level MCP servers, then review and accept the file. One shortfall (restore is not a single action). Sources

The export contains everything that defines the agent: configuration, instructions, memory, action history, logic, schedules and triggers, files, any identity, and credentials or a way to re-link each integration without any provider-registered client or broker. Derived data (embeddings, indexes, summaries) is accompanied by the source it was derived from.

The only export is the session file (conversation, tool calls with arguments, metadata). Config, memory, prompt overrides, skills/recipes, schedules and credentials are not in it. All of these stay on the owner's disk at documented paths, the provider holds none of them, and integrations re-link with the owner's own keys. So neither fail condition is literally met, but more than one class is missing from the export, so this does not reach pass. Sources

The spec places config, instructions, memory, loops, timers, triggers, lambdas, files, messages, audit, logs, sealed identity keys and per-agent credentials in the .adf file; the project holds nothing. Items outside the file (app-level provider keys, app-scoped MCP credentials, WhatsApp session directory, runtime settings) can be re-linked by the owner without any ADF-registered client (MCP OAuth uses dynamic client registration; subscriptions use OpenAI/xAI client ids). A customised global base prompt is an app setting and does not travel; recorded, not counted, because the default install uses the shipped prompt. Sources

With a non-provider model configured per P4, the exported agent runs indefinitely without any service, account, licence or payment controlled by the original provider, and the owner holds a perpetual, irrevocable right to run it. No provider action, inaction or disappearance can disable or degrade it.

The runtime is Apache-2.0 and has no licence check, account or provider service in the core. Local inference is a default build feature, and the docs root and other endpoints are configurable. Settled only hands-on; credited here from source. Sources

The runtime is MIT-licensed with no licence check, account or ADF-run relay; the only ADF-project endpoints (GitHub update check, MCP registry, skill catalog, online docs) are optional, have off switches or bundled fallbacks, and are not needed to start or act. Mesh uses LAN, tailnet or owner-chosen addresses. Desk grade; a provider-blocked run is required at certification. Sources

The owner can choose any model that meets a documented minimum, including a local open-weight model, without provider involvement, and every capability of the agent (tools, triggers, messaging, memory, automation) keeps working. A swap may lower reasoning quality but never removes a capability.

Any provider can be chosen, including built-in local inference and Ollama. A tool shim and a tiny-model prompt cover models without native tool calling, and dictation has a local Whisper option. But Desktop's Live Voice feature has only an OpenAI realtime implementation, so it is lost on other models. Sources

Tool calling is the documented minimum and local open-weight models (Ollama, LM Studio, vLLM, llama.cpp) are first-class provider options; triggers, timers, lambdas and middleware run in the runtime. However, video input is implemented by injecting OpenAI-format video_url parts and is documented only for hosted providers (OpenRouter, Gemini), and no test against a local model is published, so under G12 at least one capability is not shown to survive a local model. Sources

Whatever the P2 export contains stays obtainable, current to the moment of termination, after account suspension or ban, a lapsed subscription, a price change, an acquisition, or the provider shutting down.

All agent state is local-first on the owner's device, and there is no account whose termination could cut off access. Sources

State is local-first: the agent is a SQLite file on the owner's disk with no ADF account or hosted copy, so no termination, ban or shutdown by the project can remove access to it. Sources