herdr — What is it?

Herdr is a terminal-based agent multiplexer designed to manage and interact with AI coding agents, providing a workspace and terminal management system for efficient coding workflows.

⭐ 3,966 Stars 🍴 253 Forks Rust AGPL-3.0 Author: ogulcancelik
Source: README View on GitHub →

Why it matters

Herdr is gaining attention due to its unique approach of integrating AI coding agents within a terminal environment, offering a seamless and efficient workflow for developers. Its lightweight nature, agent awareness, and integration capabilities make it stand out in the terminal multiplexer space.

Source: README, project traits

Core Features

Workspaces, tabs, and panes

Herdr allows users to organize their work into workspaces, which contain tabs and panes. Each pane is a real terminal process, providing a direct view of the agent's terminal without interpretation.

Source: README
Agent awareness

Herdr provides a sidebar that shows the state of each agent (blocked, working, done, idle), allowing users to quickly identify the status of their agents.

Source: README
Session persistence

Herdr maintains session state across client detaches and server restarts, allowing users to resume their work seamlessly.

Source: README
Integration with AI coding agents

Herdr supports various AI coding agents, providing a platform for developers to interact with these agents directly from their terminal.

Source: README

Architecture

Herdr's architecture is modular, with a clear separation between the server and client components. The server manages the workspace, tabs, and panes, while the client provides the user interface and interaction. The project uses Rust for its implementation, leveraging various libraries for terminal handling, serialization, and concurrency.

Source: Code tree + dependency files

Project Knowledge Graph

Knowledge graph: project (center) + core features (inner hexagons) + key dependencies (outer chips) crossterm ratatui serde tokio Workspaces, tabs, and panesWorkspaces, tabs, a… Agent awareness Session persistence Integration with AI coding agentsIntegration with AI… herdr Project Core feature Key dependency

Center: project; inner ring: core feature modules; outer ring: key dependencies. Auto-generated from core_features and tech_stack.key_deps.

Tech Stack

LanguageRustFrameworkcrossterm, ratatui, serde, toml, tracing, tracing-subscriber, tokio
crosstermratatuiserdetokio
Not specified
Source: Dependency files + code tree

Quick Start

```bash curl -fsSL https://herdr.dev/install.sh | sh or install with homebrew: brew install herdr or download the binary from [releases](https://github.com/ogulcancelik/herdr/releases). requires linux or macos. herdr ```
Source: README Installation/Quick Start

Use Cases

Herdr is suitable for developers who work with AI coding agents and require a terminal-based interface for managing their workflows. It is useful for scenarios such as coding with AI agents, managing multiple terminal sessions, and organizing work into structured workspaces.

Source: README

Strengths & Limitations

Strengths

  • Strength 1: Efficient terminal-based management of AI coding agents
  • Strength 2: Seamless session persistence across client detaches and server restarts
  • Strength 3: Integration with a variety of AI coding agents

Limitations

  • Limitation 1: Limited documentation on advanced features
  • Limitation 2: May require some learning curve for users unfamiliar with terminal-based tools
Source: Synthesis of README, code structure and dependencies

Latest Release

v0.6.6 (2026-05-31): Added custom command keybindings with optional descriptions, pane copy mode, and native agent session restore. Fixed macOS server startup issues with large restored sessions.

Source: GitHub Releases

Verdict

Herdr is a promising project for developers looking to integrate AI coding agents into their terminal workflows. Its unique features and integration capabilities make it a valuable tool for managing complex coding tasks efficiently.

Transparency Notice
This page is auto-generated by AI (a large language model) from the following public materials: GitHub README, code tree, dependency files and release notes. Analyzed at: 2026-06-01 18:32. Quality score: 85/100.

Data sources: README, GitHub API, dependency files