Agentes de IA
Rakazo
Rakazo — an open-source, self-hosted AI agent for persistent teammates: bring your own model and computer, run it on web, desktop, iPhone and Android
★ 4,1 mil estrelasApache-2.0
Ver o guia →open-multi-agent é um projeto de agentes de IA de código aberto de open-multi-agent (open-multi-agent/open-multi-agent no GitHub). Tem 6.990 estrelas no GitHub e 2.437 forks, é publicado sob a licença MIT e foi atualizado pela última vez no GitHub em 8 de outubro de 2026.
Open Multi Agent — a self-hosted TypeScript agent runtime with durable approvals and verifiable run records.
Decide your governance floor upfront: which actions need human approval, what the egress policy allows, and which providers' keys the runtime may use.
Clone open-multi-agent/open-multi-agent from GitHub (or use the install path in the run steps below) and skim its README for the parts relevant to your goal.
Add @open-multi-agent/core to your TypeScript project from npm and configure your provider adapters.
Define one agent and one task, run it, then inspect the verifiable run record and test that an approval-gated step really pauses.
Approval gates survive restarts — a run can pause for a human decision and resume later, rather than living only in memory.
Every run produces a verifiable record, so what agents did can be audited after the fact.
A baseline of governance controls that child policies can only tighten, with enforcement that fails closed when a transport cannot be enforced.
Built-in adapters for major hosted providers and Chinese cloud models, plus local runtimes (Ollama, vLLM, llama-server) through baseURL and any OpenAI-compatible endpoint.
Set offline or allowlist egress, checked before a built-in adapter connects — your keys, your models, your network rules.
No telemetry, no OMA backend or account, and no analytics, license or update requests — it is a library you host.
Get the source from the official GitHub repository for open-multi-agent.
git clone https://github.com/open-multi-agent/open-multi-agent.git
cd open-multi-agentAdd the core package to your project exactly as the README's get-started section shows.
npm install @open-multi-agent/coreFollow the README get-started example to define an agent/team and run your first task with its journal enabled.
# Use the README get-started code (runAgent / runTeam) in your project — see READMEThe open-multi-agent project, through the open-multi-agent/open-multi-agent repository, with the site at open-multi-agent.com.
Yes — the project is open source under the MIT license as recorded for this listing. You can use, modify and distribute it under the MIT terms.
You supply the model access — provider API keys for hosted models, or local runtimes such as Ollama for free local inference; the runtime itself is free and MIT licensed.
Typical frameworks optimise for getting agents running quickly. OMA optimises for governing them — durable approvals, verifiable journals, egress policy and zero telemetry — which matters once agents act in production under real accountability.
Clone the repository (or use the documented installer), follow the install and configure steps on this page, and run the smallest real task first — define one agent and one task, run it, then inspect the verifiable run record and test that an approval-gated step really pauses.
Rakazo — an open-source, self-hosted AI agent for persistent teammates: bring your own model and computer, run it on web, desktop, iPhone and Android
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Grátis e aberto: pegue o código-fonte, dê uma estrela no repositório e comece a criar.