Agent Desktop Ffi
C-ABI Cross-Language Call Agent Desktop Platform Adapter
Install & Use
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This Skill addresses the common pain points of cross-language calls to the Agent Desktop platform adapter. In real-world development, developers often need to directly manipulate Agent Desktop's capabilities—such as window management, UI element snapshotting, and action execution—from various languages like Python, Node.js, Go, and Swift. However, invoking these via CLI subprocesses each time is inefficient and makes fine-grained state control difficult. This FFI library provides a C-ABI standard dynamic link library, enabling all languages that support C interfaces to directly call ad_* functions without inter-process communication.
Usage is straightforward: simply compile the cdylib in your project and link it to the target language. Taking Python as an example, first generate libagent_desktop_ffi.dylib (or .so/.dll) via cargo build --profile release-ffi -p agent-desktop-ffi. Then, use ctypes to load the library and call ad_init for ABI handshake, create an adapter, invoke ad_snapshot to obtain a snapshot, parse the ref ID from the JSON, and finally execute actions via ad_execute_by_ref and release strings. The entire workflow is fully consistent with the official CLI output but eliminates the overhead of process startup for each call.
It is ideal for full-stack developers, agent developers, and desktop automation teams requiring high-frequency, low-latency calls to Agent Desktop operations. Teams already using multiple programming languages to build cross-platform toolchains can integrate uniformly via the C-ABI interface, avoiding redundant CLI wrappers. For scenarios requiring fine-grained control over session isolation and persistent connections, the library offers advanced interfaces like ad_adapter_create_with_session.
We recommend using this FFI library uniformly when building cross-language desktop automation toolchains. Note that it primarily targets C-ABI-compatible languages; for languages without direct C bindings like pure Java or C#, additional JNI or P/Invoke wrappers may be needed. Additionally, ad_init must be called before any other operations for version validation to ensure the dynamic library and header file ABI versions match; otherwise, an error will be returned and services refused.
Key Features
Compared to directly invoking Agent Desktop via CLI subprocesses, this FFI library eliminates the process creation and teardown overhead per call, supports long-lived session adapters, and enables zero-copy data transfer via C-ABI, delivering significant performance improvements.
Limitations
Requires the Rust toolchain to compile the cdylib, and the generated dynamic library only supports C-ABI-compatible languages (e.g., Python ctypes, Node ffi-napi, Go cgo, Swift, C++, Ruby fiddle). Additional wrappers are needed for pure Java or C#.
FAQ
How to ensure the ABI version consistency between the header file and the dynamic library?
Call `ad_init(AD_ABI_VERSION_MAJOR)` for version validation. If `AD_RESULT_ERR_INVALID_ARGS` is returned, it indicates a mismatch, and loading should be aborted. You can also use `ad_abi_version()` to retrieve the dynamic library's actual Major version for diagnostics.
What should be noted when calling from different languages?
Each language must correctly declare function signatures and data structures according to its C-ABI binding method. The repository provides a complete Python ctypes example (`tests/ffi-python/smoke.py`). For other languages, refer to the function declarations and parameter types in the C header file `agent_desktop.h`.
Installation guide for AI assistants
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