Hive
Hive is an open-source, terminal-first, async multi-agent coding pipeline that turns a rough idea into a merge-ready pull request using a transparent folder-based workflow, a background daemon, and a power-user TUI.
https://hivecli.sh/?ref=producthunt
Product Information
Updated:Jun 16, 2026
What is Hive
Hive (hive-cli) is a free, MIT-licensed tool that automates the end-to-end software delivery loop—from an initial idea to a ready-to-merge PR—directly from your terminal. It’s designed for developers who want autonomous, inspectable agent workflows without living in chat threads: you capture an idea, answer targeted questions in your editor, and Hive orchestrates the rest. Hive integrates with your existing Git/GitHub setup (e.g., authenticated gh) and can run different agent CLIs per stage (Claude by default, with options like Codex or Pi).
Key Features of Hive
Hive is an open-source, terminal-first, async multi-agent coding pipeline that turns a rough idea into a merge-ready pull request by moving each task through a transparent, folder-based workflow (brainstorm → plan → execute → review → finalize). It runs multiple agents in parallel in the background via a daemon, prompts you only when decisions are needed (answered in your editor), and produces durable markdown artifacts at every stage so the process is inspectable, editable, and handoff-friendly. Hive integrates with configurable agent CLIs (Claude by default, plus others like Codex/Pi), supports autonomous repo “patrol” and PR “babysitting,” and can be driven via a TUI or optionally through a Telegram bot for mobile approvals and idea capture.
Nine-stage idea-to-PR pipeline: Runs a structured workflow from inbox capture through brainstorm, plan, execution in an isolated worktree, PR creation, review hardening, artifact collection, and finalization to a ready-to-merge PR.
Folder-as-state transparency (artifact-driven): Each task is a folder whose location represents state; every stage outputs durable markdown artifacts (plans, reviews, PR metadata) that you can read, edit, or hand to another agent—no black-box database.
Async daemon + parallel task queue: Advances multiple tasks concurrently in the background; the TUI highlights only the tasks that need your input, enabling low-interruption, asynchronous development.
Configurable multi-agent execution: Stages run on configurable agent CLIs—Claude by default, with support for alternatives (e.g., Codex or Pi)—so you can choose different models/tools per stage.
PR babysitter and repo patrol automation: Opt-in automation can patrol a repo for candidate improvements and open PRs, and keep existing PRs green via bounded repair attempts and auto-rebases, handing off when stuck.
Terminal-first UX with optional Telegram bot: A power-user TUI/CLI workflow for capturing ideas and approving stage transitions, plus a Telegram bot for capturing inputs and approving work from a phone (including voice/photo/doc ingestion).
Use Cases of Hive
Product feature delivery for software teams: Convert loosely defined feature ideas into scoped plans, implemented code, and reviewed PRs with minimal synchronous coordination—useful for fast-moving product engineering.
Open-source maintenance and contributor workflows: Automate issue-to-PR pipelines, generate review artifacts, and use babysitter to keep PRs mergeable—helpful for maintainers managing many parallel contributions.
Internal developer platform / tooling teams: Standardize how internal tools and platform changes are proposed, planned, executed, and reviewed, leaving auditable artifacts for compliance and cross-team handoff.
DevOps and reliability automation: Use patrol/babysitter patterns to propose fixes (e.g., CI breakages, dependency bumps), open PRs, and keep them rebased and green while engineers approve decisions asynchronously.
Startup prototyping and rapid iteration: Run multiple experiments in parallel: capture ideas quickly, let agents draft implementation and PRs, and only step in for key product decisions or clarifications.
Mobile-first approvals for distributed teams: Capture ideas and approve task progression via Telegram while away from the workstation, enabling asynchronous progress across time zones.
Pros
Highly inspectable workflow: artifacts are plain files (markdown) and task state is visible via folders, improving trust and handoff.
Strong async/parallel execution: daemon-driven queue reduces babysitting and keeps multiple efforts moving concurrently.
Flexible agent/tool choice: integrates with different agent CLIs per stage, allowing optimization for cost/performance by task type.
End-to-end PR automation: includes execution in isolated worktrees, PR opening, review hardening, and PR upkeep (rebases/repairs).
Cons
Token-heavy by default: multi-agent stages can be expensive, making it less suitable for cost-sensitive users.
Terminal-first and daemon-based: requires comfort with TUI/CLI workflows and running a background daemon locally.
Requires external tooling setup: depends on Ruby, git, authenticated GitHub CLI, and the chosen agent CLIs (e.g., Claude/Codex).
How to Use Hive
Decide which “Hive” you mean (Apache Hive vs Hive CLI coding tool): The sources include both Apache Hive (data warehouse on Hadoop; commands like bin/hive, Beeline) and a separate product called Hive CLI (hivecli.sh) for multi-agent coding. Pick the one you intend to use before proceeding.
Apache Hive: Verify prerequisites: Ensure Hive is installed and HIVE_HOME is set. If you are on Hive 3+, plan to use Beeline (HiveServer2 client) rather than the deprecated Hive CLI.
Apache Hive: Start an interactive session (legacy Hive CLI): Run: $HIVE_HOME/bin/hive. If run without -e or -f, it enters interactive shell mode; terminate statements with a semicolon (;).
Apache Hive: Run a query from the command line (non-interactive): Use -e for inline SQL: $HIVE_HOME/bin/hive -e 'select ...;'. This is useful for one-shot commands or scripting.
Apache Hive: Run an HQL script file (non-interactive): Use -f to execute SQL from a file: $HIVE_HOME/bin/hive -f /path/to/script.hql. This is the standard way to run saved scripts.
Apache Hive: Initialize a session with a startup SQL file: Use -i to run initialization SQL automatically before other commands: hive -i /path/to/init.sql (can be combined with -e or -f).
Apache Hive: Pass configuration properties at runtime: Use --hiveconf (or -hiveconf) to set properties: hive --hiveconf hive.exec.scratchdir=/opt/my/hive_scratch --hiveconf mapred.reduce.tasks=1 -e 'select ...;'.
Apache Hive: Reduce output noise for scripting: Use silent mode (-S) so only data is emitted in interactive shell contexts: hive -S (or combine where supported).
Apache Hive: Enable more logging for debugging: Override logging via hiveconf, e.g.: $HIVE_HOME/bin/hive --hiveconf hive.root.logger=INFO,console. Default logging often goes to /tmp/$USER/hive.log at WARN.
Apache Hive (recommended): Use Beeline (HiveServer2 client): Beeline is the JDBC-based CLI for HiveServer2 and is recommended/required in newer Hive distributions. Start Beeline and connect to HiveServer2 using a JDBC URL (exact URL depends on your cluster setup). Then run queries or scripts similarly via Beeline options.
Apache Hive: Example DDL/DML workflow in the shell: In an interactive session, you can create databases/tables, load data, and query. Example: LOAD DATA INPATH '/user/myname/kv2.txt' OVERWRITE INTO TABLE invites PARTITION (ds='2008-08-15');
Hive CLI (hivecli.sh): Install on macOS via Homebrew: Run: brew install ivankuznetsov/hive/hive.
Hive CLI (hivecli.sh): Install on Arch Linux via AUR: Run: yay -S hive-bin.
Hive CLI (hivecli.sh): Install on Linux via install script: Run the provided installer: tmpdir="$(mktemp -d)" && trap 'rm -rf "$tmpdir"' EXIT && curl -fsSL https://raw.githubusercontent.com/ivankuznetsov/hive/v0.3.0/install.sh -o "$tmpdir/hive-install.sh" && bash "$tmpdir/hive-install.sh".
Hive CLI (hivecli.sh): Prepare prerequisites: Ensure Ruby 3.4, git, authenticated gh, and the agent CLIs you plan to use (e.g., claude, codex) are installed.
Hive CLI (hivecli.sh): Initialize Hive in a repository: From your project directory: cd ~/Dev/your-project; then run: hive init . (choose launch/permission mode and enroll the daemon).
Hive CLI (hivecli.sh): Open the TUI dashboard: Run: hive tui. Use the dashboard to manage tasks; press 'n' to capture a new idea.
Hive CLI (hivecli.sh): Understand the stage-based workflow: Each task is a folder that moves through stages: inbox → brainstorm → plan → execute → open-pr → review → artifacts → finalize → done. Moving the folder forward is the approval gesture; each stage leaves markdown artifacts.
Hive CLI (hivecli.sh): Let the daemon run tasks asynchronously: Hive advances multiple tasks in parallel in the background; you typically only need to answer questions in the generated docs, then approve by moving the task to the next stage.
Hive FAQs
Hive is an open-source (MIT) terminal-first tool that turns a rough idea into a merge-ready pull request by running it through an async multi-agent pipeline (brainstorm, plan, execute, review, finalize). It advances tasks in parallel in the background and uses a TUI where you answer questions in markdown docs.
Hive Video
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