hcom — Orchestrates AI agents to message, observe, and spawn each other across terminals.
Analyzed by Sai Pavan Gopularam · AI · Agents · View on GitHub
- Stars: 553
- Forks: 84
- Commits last 30 days: 100
- Health: Active (100 commits this month)
- Language: Rust
- License: MIT
What It Is
Imagine a central nervous system for your AI coding agents. `hcom` is a command-line interface tool that lets different AI agents, like Claude, Codex, or Gemini, communicate, observe each other's actions, and even spawn new agents. It acts as a broker, routing messages and activity through a local SQLite database, allowing agents to collaborate across multiple terminal windows or even different machines.
This system solves the problem of isolated AI tools. Instead of manually copying outputs or writing complex scripts to stitch agents together, `hcom` provides a native way for them to interact. It enables sophisticated multi-agent workflows, automating tasks like collaborative code review, complex data analysis, or continuous monitoring, transforming your AI assistants into a coordinated team.
License Verdict
MIT License — Build and Sell Freely — Commercial Use Approved • No Copyleft Restrictions
The MIT License permits you to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the software. This means you can freely incorporate `hcom` into proprietary products and services, including SaaS offerings, without any obligation to open-source your own code.
How to Use It
Install `hcom` via a package manager or script. Then, simply prepend `hcom` to your AI agent's command to bring it into the multi-agent network. You can launch multiple agents in different terminals and prompt them to interact.
Prerequisites:
- Python 3.x (for uv)
- uv (Python package installer)
- Homebrew (macOS/Linux optional)
Estimated setup time: 10 minutes.
uv tool install hcom
hcom claude
hcom codex
# In one terminal, prompt: ask the other agent their favorite cakeWhat I'd Build With This
AI Dev Team Orchestrator (micro-saas)
Build a simple web interface that allows users to define and launch multi-agent workflows for specific dev tasks, like 'bug squash' (one agent identifies, another fixes, a third tests). Users upload their code, and your service orchestrates the `hcom`-powered agents. Customers would be indie developers or small teams looking for automated code assistance.
Effort: 3 Weeks Build Time · Target: Indie Hackers, Small Dev Teams · Pricing: $49/month for 50 agent-hours
Multi-Agent Workflow Design Studio (saas)
Develop a visual drag-and-drop interface where users can design complex multi-agent pipelines. Users select from pre-integrated AI agents (or bring their own via API keys), define their communication protocols, and set up triggers and conditions. This platform would manage the `hcom` instances and execution, providing analytics on agent performance and output. Target: AI developers, SMBs, and research institutions.
Effort: 3 Months Build Time · Target: AI Developers, SMBs · Pricing: Tiered plans from $99/month to $499/month based on usage and features
Internal AI Operations Hub (enterprise)
Offer a custom, on-premise or private cloud deployment of an `hcom`-based solution for large enterprises. This hub would allow internal teams to securely deploy and manage fleets of AI agents for tasks like automated compliance auditing, internal documentation generation, or complex data pipeline orchestration. The service would include robust access control, logging, and integration with existing enterprise systems.
Effort: 6 Months Build Time · Target: Large Corporations (IT/DevOps) · Pricing: $50,000+ custom annual contracts
Sai Pavan Gopularam's Take
This is a fascinating project that tackles the messy problem of getting AI agents to work together. Instead of abstract API calls, it grounds agent interaction in actual terminal sessions, making it surprisingly intuitive. I see a clear path to monetizing this by offering specialized multi-agent workflow templates as a micro-SaaS, potentially generating $5k/month by selling access to these pre-built 'AI teams' for specific dev tasks.
Watch Out For
- Agent Compatibility and Setup: `hcom` orchestrates agents, but you still need to install and configure each individual AI CLI tool (e.g., Claude, Codex) separately for `hcom` to use them. This adds an initial setup layer for each agent type.
- Security of Relay Tokens: When using `hcom`'s cross-device relay feature, the generated token acts like an API or SSH key. It needs to be handled with extreme care and secured properly to prevent unauthorized access to your agent network.
- Orchestration Complexity: While `hcom` simplifies agent communication, designing effective multi-agent workflows still requires careful prompt engineering and understanding how agents interact. It's not a 'set and forget' solution for complex tasks, and iteration will be key.
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