Gemini CLI vs Seeknal

Gemini CLI is Google's open-source AI agent that runs directly in your terminal. Built on Apache 2.0 and now at v0.39.0, it ships with Gemini 3.1 Pro by default, native Google Search grounding, and full MCP (Model Context Protocol) support. Individual developers get 1,000 model requests per day for free on a personal Google account — no API key required to start. The tool is modeled around a GEMINI.md convention (similar to Claude's CLAUDE.md), supports per-project and per-user configuration, and introduced "Chapters" in v0.38 — a way to organize long agentic sessions by intent and tool usage. The April 23 release added a /memory command to review and patch extracted skills from sessions, along with enhanced Plan Mode requiring explicit confirmation before skill execution. It's Google's direct answer to Claude Code and OpenAI Codex CLI — and arguably the most generous free tier of the three. Google SREs are already using it in production to resolve live infrastructure incidents, which says something about internal confidence. For developers who want a Gemini-native agentic workflow without paying per token, this is the most practical option available today.

Seeknal is a Data & ML CLI designed for teams running agent-driven data pipelines. The core workflow follows three verbs: Organize (define pipelines in YAML or Python), Expose (materialize data to PostgreSQL and Apache Iceberg), and Action (query and transform data in natural language). It uses a draft, dry-run, apply progression that gives teams control before changes hit production. The natural language query layer is what sets Seeknal apart from standard data pipeline tools. Instead of writing SQL to explore a freshly materialized table, you describe what you want — and Seeknal translates that to the appropriate query against your Postgres or Iceberg target. The combination of structured pipeline definition (YAML/Python) with flexible natural language exploration is designed for the reality that data teams include both engineers who want explicit control and analysts who want fast iteration. The 'built for the agent world' framing reflects a genuine architectural choice: Seeknal's API is designed to be called programmatically by AI agents, not just by humans with keyboards. This matters because data pipeline management is increasingly something agents need to do autonomously — fetching fresh context, materializing results, and querying outputs — without human intervention at each step. Seeknal launched on Product Hunt today targeting teams that have adopted agentic workflows but still treat their data infrastructure as human-operated.