Agent Desktop Ffi
C-ABI bindings enable cross-language desktop automation calls
Install & Use
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Help me install this AI Skill: Agent Desktop Ffi. It is used for: C-ABI bindings enable cross-language desktop automation calls Full Skill content: https://321skill.com/skills/agent-desktop-ffi-x-2/raw/index.md Read that page and install it.
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This Skill addresses the performance and complexity issues of repeatedly spawning agent-desktop CLI subprocesses in multi-language development. In practice, requiring each language to execute desktop automation operations by launching a subprocess results in high latency and significant resource waste. Agent Desktop Ffi provides a C-ABI-bound dynamic library, allowing languages like Python, Node.js, Swift, Go, C++, and Ruby to directly call the core functionalities of agent-desktop, eliminating the process overhead incurred with each call.
Usage is straightforward: simply run cargo build --profile release-ffi -p agent-desktop-ffi in your Rust workspace to compile the dynamic library (.dylib/.so/.dll). Then, load the library via your language's FFI mechanism and call the ad_* series of functions. The standard observe-act workflow is: first call ad_init for ABI handshake, then create an adapter (ad_adapter_create or ad_adapter_create_with_session), followed by ad_snapshot to capture a UI snapshot. Parse the ref ID from the returned JSON, and finally execute actions like clicks via ad_execute_by_ref. Remember to call ad_free_string to release memory.
It is ideal for teams or individuals needing deep integration of desktop automation into various programming languages. This is especially useful for developers already using agent-desktop who wish to reuse the same desktop operation capabilities across different language ecosystems (e.g., Python data scripts, Node.js backend services, Go microservices). For agent development projects requiring cross-language collaboration, this library can significantly reduce integration costs.
It is recommended to use this FFI library as the underlying communication layer when developing cross-language desktop automation tools. Note that it requires a Rust toolchain for compilation, and each language must correctly configure the dynamic library loading path. Memory management follows a strict "who allocates, who frees" rule. Refer to the ownership.md document in the official references to avoid memory leaks.
Key Features
Unlike the agent-desktop CLI approach, Agent Desktop Ffi eliminates the overhead of creating a subprocess for each operation by directly linking the dynamic library, reducing latency by an order of magnitude. It supports native calls from various languages such as Python ctypes, Node ffi-napi, and Go cgo, rather than being limited to a command-line interface.
Limitations
Requires a Rust compilation environment to build the cdylib, and each language must manually manage dynamic library loading paths and memory deallocation (following the `ad_free_string` rule). Not supported in pure WebAssembly environments.
FAQ
What's the difference between this library and using the agent-desktop CLI directly?
The CLI spawns a new process for each call, resulting in high latency. The FFI library directly links the dynamic library, enabling function calls with zero process overhead, making it suitable for high-frequency operation scenarios.
How do I use this FFI library in Python?
After compiling libagent_desktop_ffi.dylib, load the library using Python ctypes and call functions like `ad_init` and `ad_adapter_create`. Refer to the tests/ffi-python/smoke.py example for guidance.
Installation guide for AI assistants
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