iii-hq

iii-hq / agentos

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Agent Operating System built on three primitives: Worker, Function, Trigger

agentsos.sh
25
0
100% credibility
Found Mar 11, 2026 at 19 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
TypeScript
AI Summary

AgentOS is a comprehensive open-source platform for orchestrating teams of AI agents with built-in tools, memory, security, scheduling, and multi-agent coordination features.

How It Works

1
🔍 Discover AgentOS

You hear about AgentOS, a system that lets everyday people build teams of smart AI helpers for tasks like coding, research, or planning.

2
📥 Install easily

Run a simple one-line command to download and set up everything on your computer.

3
🔗 Connect AI brains

Link your favorite AI service with a quick setup so your helpers can think and respond.

4
🚀 Launch your team

Start the system and watch dozens of ready-made AI agents come online, each specialized for different jobs.

5
Pick your path
🗣️
Chat right away

Talk to agents like the coder or researcher to get instant help on tasks.

🎨
Build custom

Use templates to create personalized AI workers for your unique needs.

6
👥 Team up agents

Let agents collaborate in groups or swarms to tackle bigger projects together.

✅ AI works for you

Your AI team runs autonomously, handling work while you relax and check results anytime.

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Star Growth

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AI-Generated Review

What is agentos?

Agentos is an agent-centric operating system that runs swarms of AI agents on a lightweight bus of workers, functions, and triggers, handling everything from tool calls and memory to inter-agent coordination and self-evolving code. Built primarily in TypeScript with Rust for performance-critical paths and Python for embeddings, it supports 25 LLM providers, 60+ built-in tools like file ops and web search, and 45 ready-to-spawn agent templates for tasks like coding or research. Developers get a full CLI with 50+ commands, TUI dashboard, and OpenAI-compatible API without framework lock-in.

Why is it gaining traction?

It stands out with no-magic primitives that let agents evolve their own functions via LLM code gen in sandboxes, plus DAG-based artifact sharing and coordination boards for swarms—features rivals like LangChain lack in a unified OS. The polyglot design (Rust hot paths, TS iteration) delivers speed without complexity, and extras like session replay, budget enforcement, and 40 channel adapters make multi-agent setups production-ready fast. Early adopters hook on the quickstart CLI and 2,700+ tests for reliable agent github actions or claude integrations.

Who should use this?

AI engineers building agent operating models for devops, research, or customer support swarms; teams migrating from agent github copilot or openai setups needing RBAC security and tool policies; indie hackers prototyping with templates like ai-engineer or rapid-prototyper.

Verdict

Try it for ambitious multi-agent experiments—solid docs, CLI, and tests make the 1.0% credibility score (18 stars) forgivable for an early project, but wait for more adoption before production. Pairs well with agent github claude or copilot for agent operating procedures.

(198 words)

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