LinkedIn CLI is a high-performance, open-source command-line tool and programmatic automation interface designed specifically for extracting public profile details and activity posts from LinkedIn via Apify. Maintained under the SpaceCorps open-source engineering collective, the project originated to provide developers and autonomous AI agents with a single, standalone binary capable of operating seamlessly across macOS, Linux, and Windows without runtime dependencies or heavy interpreter overhead.
Modern data pipelines and autonomous AI agents require rapid feedback loops, predictable startup latencies, and deterministic credential storage. The original prototype CLI was implemented as a .NET global tool (Linkedin.Console) by Niels Bosma. While functional, modern deployment environments and agent tool loops benefit immensely from static native binaries that initialize in 1 to 3 milliseconds rather than incurring the multi-millisecond startup latencies of managed runtimes. In 2026, the CLI was completely re-architected in native Rust (Edition 2024) to achieve sub-millisecond execution, minimal memory consumption, and zero runtime dependencies.
LinkedIn CLI is built around three uncompromising tenets: safety, determinism, and speed. Unlike conventional scraping scripts that store raw API keys in plaintext environment variables or arbitrary dotfiles, LinkedIn CLI integrates natively with host operating system credential vaults—such as macOS Keychain, Windows DPAPI, and the Linux Secret Service daemon. Furthermore, multi-account management prevents credential confusion across organizations and automation environments.
The CLI orchestrates proven, industry-standard Apify actors (harvestapi~linkedin-profile-scraper and supreme_coder~linkedin-post) using synchronous dataset retrieval. Every command supports granular section-level filtering (--include) to minimize bandwidth, latency, and LLM context window consumption.