Herdr: Multi-Agent Terminal Management Tool for Effortlessly Managing Claude Code/Codex Multitasking

When running multiple Claude Code or Codex Agents simultaneously, window chaos and difficulty tracking status is a problem nearly every developer encounters. Herdr, a terminal management tool built specifically for AI Agents, might fundamentally change your multi-Agent workflow.

Why You Need a Professional Multi-Agent Terminal Management Tool

As AI programming assistants become more widespread, an increasing number of developers are running multiple Agents in the terminal simultaneously to handle different tasks. Whether using Claude Code for code analysis or Codex for project refactoring, multi-Agent parallelism has become a common practice for boosting development efficiency.

In fact, AI programming assistants have gone through three distinct phases: from initial single-turn Q&A (like early GitHub Copilot code completion), to multi-turn conversational programming (like ChatGPT's code mode), to today's autonomous Agent mode. In Agent mode, AI no longer passively waits for user instructions—it can proactively plan tasks, read file systems, execute commands, and adjust strategies based on results. This autonomy greatly expands the capability boundaries of individual Agents, but it also means that when multiple Agents work simultaneously, their respective states, outputs, and resource usage all need to be effectively managed. This is precisely a scenario that traditional terminal tools were never designed to handle.

However, traditional terminal tools fall short when facing multi-Agent scenarios. Take tmux as an example—while it's a classic terminal multiplexer, it has a steep learning curve, tedious configuration, and frequently fails to start properly. tmux (Terminal Multiplexer) was born in 2007, allowing users to create multiple sessions, windows, and panes within a single terminal window. Its core value lies in session persistence—even if an SSH connection drops, processes in tmux continue running. However, tmux's operation relies on numerous prefix key combinations (defaulting to Ctrl+B), and its configuration file .tmux.conf has obscure syntax that's extremely unfriendly to new users.

From a longer historical perspective, the evolution of terminal multiplexers can be traced back to GNU Screen in 1987, which first enabled managing multiple virtual terminals within a single terminal connection. In 2007, tmux appeared as a modern alternative to Screen, introducing more flexible pane splitting and scriptable control capabilities. In 2021, Zellij was written in Rust, attempting to lower the barrier to entry with friendlier default configurations and a WebAssembly plugin system. However, the core design philosophy of these tools has always revolved around the paradigm of "humans operating terminals"—they assume each pane runs a program that humans directly interact with. The emergence of AI Agents breaks this assumption: Agents produce output volumes far exceeding manual human operations, and multiple Agents' output streams need to be semantically distinguished and tracked, rather than simply visually divided between panes. As the video author put it: "tmux is honestly really hard to use—I spent two days learning it, and it often doesn't come up when starting."

Herdr (officially positioned as "Tools for Agent") is a dedicated terminal management tool built precisely to solve this pain point.

Herdr Core Features Explained

Workspace and Multi-Tab Pane Management

Herdr provides an intuitive workspace management interface with flexible multi-tab, multi-pane layouts. Users can create and switch windows with simple keyboard shortcuts—pressing N creates a new window, and the entire workflow is very smooth.

Unlike traditional terminal multiplexers such as tmux, Herdr doesn't run as a terminal application—instead, it provides a standalone windowed interface. You don't need to memorize complex key combinations or spend time configuring .tmux.conf files. It truly works out of the box.

Multi-Agent Visual Tracking: Herdr's Core Differentiator

Multi-Agent visual tracking is Herdr's most core feature and the most attractive aspect for developers. When you launch multiple sub-Agents through Claude Code, Herdr automatically identifies them and displays independent views for each Agent at the bottom, supporting free switching between different Agents and real-time viewing of what each Agent is executing.

Here we need to understand the technical mechanism of sub-Agents in depth. The technical foundation of the sub-Agent mechanism stems from large language models' Function Calling and Tool Use capabilities. When the main Agent receives a complex task, it uses its built-in planning ability to decompose the task into multiple parallelizable subtasks, then spawns an independent Agent process for each subtask. Each sub-Agent has its own system prompt, tool permissions, and context window, and they typically don't share state with each other. This architecture is similar to the MapReduce pattern in distributed computing—the main Agent handles task splitting (Map) and result aggregation (Reduce), while sub-Agents handle specific execution. Claude Code natively supports this multi-Agent spawning capability in its latest version, and users can control the maximum number of concurrent sub-Agents and their respective permission scopes through configuration files.

For example, when you ask Claude Code to analyze a large project, it might simultaneously launch one sub-Agent for frontend code review, another for backend logic analysis, and a third for dependency security checks. Each sub-Agent is an independent process with its own context and output stream. This parallel mechanism dramatically improves task processing efficiency but also introduces challenges in process management and state tracking—which is exactly the core problem Herdr solves.

In actual testing, the author had Claude Code launch four Agents simultaneously to analyze a project, and Herdr successfully displayed each Agent's working state independently, supporting quick switching between different views. This would be nearly impossible in a traditional terminal environment—you'd either need to open four terminal windows and switch back and forth, or just stare at a chaotic output stream.

Compatible with Mainstream CLI Agent Tools

Herdr isn't limited to a single AI programming tool—it's compatible with today's mainstream CLI Agent tools, including:

• Claude Code — Anthropic's terminal-native programming tool that can directly read project files, execute shell commands, and engage in multi-turn conversational programming. It's one of the most popular CLI programming assistants today.
• Codex — OpenAI's open-source command-line coding agent (Codex CLI) that supports autonomous code modifications in sandbox environments, emphasizing code generation safety and controllability.
• Aider — A popular open-source AI pair programming tool that supports multiple LLM backends including GPT-4, Claude, and Gemini, known for its flexibility.
• Gemini CLI — Google's command-line AI tool based on the Gemini model, deeply integrated with the Google ecosystem.

The common characteristic of these tools is that they run in terminal environments and output their work processes as text streams—which is the fundamental reason why output information gets confused during multi-Agent parallel execution. Herdr's broad compatibility means it can seamlessly integrate into your existing development workflow regardless of your tech stack preferences.

Remote Management and Mobile Support

Herdr also offers some features that exceed expectations. It supports remote sandbox environment launches and allows you to manage running Agent tasks by scanning a QR code with your phone.

The importance of sandbox environments in AI Agent scenarios far exceeds that of traditional development environments. When an Agent is authorized to execute shell commands and modify files, a single erroneous rm -rf command could cause catastrophic consequences. Modern Agent sandboxes typically employ multi-layer isolation strategies: the outermost layer is network isolation, limiting Agent access to specific network resources; the middle layer is filesystem isolation, using container union filesystems (like OverlayFS) to ensure Agent modifications don't affect the host machine; the innermost layer is process isolation, using Linux namespace and cgroup mechanisms to limit Agent process resource usage. Codex CLI runs in a sandbox by default, and all file modifications require explicit user approval before being applied to the actual project.

Herdr's remote sandbox launching essentially deploys these isolation layers in container instances on remote servers, transmitting Agent input/output streams to the local management interface via protocols like WebSocket. This means developers can offload compute-intensive Agent tasks to remote servers while the local machine serves only as a monitoring and management interface—particularly important for resource-constrained local machines or tasks requiring specific runtime environments.

This feature is highly practical in real scenarios—for example, if you've launched a long-running Agent task on a server, you can check progress at any time via your phone without staying at your computer.

Herdr Installation and Getting Started

Herdr's installation process is relatively simple and can be completed via the command line. Once installed, it launches as a standalone window with a clean, intuitive interface.

From hands-on experience, Herdr's learning curve is far lower than traditional terminal multiplexers like tmux. The basic operational logic is clear: create a window, select a tool, assign a task, switch views—the entire flow is seamless. The author mentioned encountering some minor issues during installation that required using the system's built-in launch method, but overall it didn't affect normal usage.

Use Cases and Recommendations

Herdr's greatest value is demonstrated in the following scenarios:

Multi-Agent collaborative development: When you need multiple Agents to handle different modules of a project simultaneously, Herdr's visual switching capability gives you clear visibility into each Agent's work progress.

Multi-dimensional complex project analysis: Launch multiple Agents to analyze the same project from different angles (e.g., code quality, security auditing, performance optimization), and Herdr lets you monitor all analysis processes simultaneously.

Long-running Agent tasks: Combined with remote management and mobile support, it's well-suited for scenarios involving long-running Agent tasks.

Of course, if you only occasionally use a single Agent for simple code Q&A, Herdr's advantages may not be apparent. It's better suited for developers who have deeply integrated AI Agents into their daily development workflow and frequently need multi-Agent parallel work.

Summary: An Efficiency Weapon for the Multi-Agent Era

In today's rapidly iterating AI programming tool landscape, the tooling ecosystem around Agents is also continuously improving. Herdr precisely targets the niche need of multi-Agent terminal management, replacing the tedious operations of traditional terminal tools with an intuitive visual interface. For developers who heavily use CLI Agents like Claude Code and Codex, this is an efficiency-boosting tool worth trying.

From a broader perspective, Herdr's emergence reflects an important trend: as AI Agents evolve from simple conversational assistants into intelligent entities capable of autonomously executing complex tasks, management, orchestration, and monitoring tools around Agents are forming an entirely new tool layer. The Agent management tool space where Herdr operates is rapidly taking shape. From a tech stack perspective, this field can be divided into three levels: the bottom layer is the Agent Runtime, responsible for Agent process management and resource scheduling; the middle layer is Agent Orchestration, responsible for task allocation and coordination between multiple Agents; the top layer is Agent Observability, responsible for monitoring, logging, and debugging Agent behavior. Herdr currently primarily covers the runtime management and observability layers. Similar tools include LangGraph for Agent workflow orchestration, LangSmith for Agent debugging, and E2B for Agent deployment.

Just as container technology spawned orchestration tools like Kubernetes, the proliferation of multi-Agent parallel work will inevitably spawn professional Agent management tools. This ecosystem's maturity level is similar to the container ecosystem in 2014-2015—Docker had appeared but Kubernetes hadn't yet become the standard, various tools were flourishing, and industry standards hadn't been established. Herdr is one of the early explorers in this emerging field, and the growth potential of this space has only just begun to open up.