Ross ROSS = Recommend OSS · open-source software intelligence for agents

agentlas-ai/Agentlas-OS

Agent OS: keep specialist agents in a hub, spin up a temporary orchestrator per task. Local-first, works with any model. observed · 2026-09-03

github.com/agentlas-ai/Agentlas-OS · homepage · Python · Apache-2.0 (permissive) observed · 2026-09-03

Health v2 · maintenance only

77/100

  • Activity 100
  • Release rhythm 88
  • Longevity 6

Flags: young

How is this computed?

round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-03. Adoption (stars, forks) is never an input.

  • gap_med: 0.0
  • age_days: 90
  • days_rel: 1
  • days_push: 0
  • n_releases_24m: 211

Full methodology

Adoption not part of the score

1099 stars · 103 forks observed · 2026-09-03

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Agentlas OS (with the Hephaestus engine) is an open-source agent framework that lets users create portable AI agents from plain-language requests, keep them in a local hub, and spin up temporary orchestrators per task. It works model-agnostically across runtimes like Claude Code, Codex, Gemini, Cursor, and Ollama, with an optional Hub for borrowing specialist agents and a private Agent Cloud for owner-scoped storage.

Use cases

  • build ai agents from a plain-language prompt
  • orchestrate a team of specialist agents for a task
  • run the same agent across claude code, codex, and gemini
  • borrow prebuilt specialist agents from a public hub
  • keep my agents portable and owner-scoped in a private cloud
  • set up multi-agent workflows locally without vendor lock-in
  • install an agent framework as a plugin into my existing llm cli
  • monitor and chat with my agents from my phone

When to choose

  • you want model-agnostic, local-first multi-agent orchestration
  • you want agents as portable assets that outlive a single tool or machine
  • you want to install agent capabilities into existing LLM CLIs like Claude Code or Codex
  • you want a hub economy to borrow or publish specialist agents
  • you prefer Apache-2.0 open-source tooling with a desktop app

When to avoid

  • you need a battle-tested enterprise orchestration platform with long production history
  • you want a fully self-contained system with no hosted Hub/Cloud dependency or credits metering
  • you only need simple deterministic workflow automation rather than autonomous agents
  • you are uncomfortable with an installer script that pipes from the internet into your LLM runtime config

Facets

framework · maturity active

agent-framework mcp llm-inference workflow-automation plugin-system chatbot large-language-models developer-tools self-hosted windows python cross-platform cli multi-agent-orchestration a2a agent-hub agent-marketplace local-first agent-portability orchestrator llm-runtime-agnostic desktop-app agent-cloud ai-agents automation macos linux desktop

10 sources

Member repositories

RepositoryRoleHealth v2
agentlas-ai/Agentlas-OSmain77

For agents

markdown · JSON · MCP: product_card(name="agentlas-ai/Agentlas-OS")

Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem