$ man babuba
Install the gateway, boot an agent, hook up your channels, set the rules. Fifteen minutes to a running AI workforce — everything below is what you actually do.
Three steps from nothing to a running agent. No cloud account, no deployment pipeline, no per-seat math.
$ babuba deploy --license LIC.TEAM-XXXX-XXXX gateway booting .......... OK channels: whatsapp · telegram · gmail · web CONNECTED agents: make your own — ∞ available
Babuba is one gateway process — one port (18793) — hosting everything:
babuba gateway (127.0.0.1:18793) ├─ router — reads every message, picks the agent + workflow ├─ agents — one per department; own memory, tools, rules ├─ channels — whatsapp · telegram · gmail · web chat ├─ workflows — department processes as approval graphs ├─ cron — scheduled jobs, reports, scans ├─ admin panel — agents, approvals, guardrails, RBAC, logs └─ state — SQLite + memory, all on your machine
The router decides which agent handles an incoming message — by channel binding, role scope or default. Workflows can start automatically when a message matches a process graph.
The single process that runs the whole system on your hardware. Install once — the router, agents, channels, workflows and admin panel all live inside it.
One per department. Each agent is an LLM equipped with its own SOUL.md (identity and system prompt), MEMORY.md (long-term memory), tools, guardrails and approval rules. Agents are isolated from each other — finance can't see HR's data.
A LangGraph-based message router. It classifies intent, picks the right agent and can start a workflow. You don't wire rules — the router learns the routing from your agent set.
Talk to agents where you already work: WhatsApp Web, Telegram, Gmail, Slack and web chat. Same agent, same memory, every channel.
Department processes expressed as graphs — with human approval steps. A payment workflow, an onboarding workflow, a compliance review: each step can require a person's tap before it proceeds.
Scheduled jobs on the gateway: daily backups, state cleanup, self-diagnosis, market scans, reports. They run whether or not anyone is chatting.
# create — boots an agent with identity, memory, tools, channels $ babuba agents create --name finance --dept accounts generating SOUL.md ........ done (0.4s) agent online .............. RUNNING (2.1s) # add users — the whole team, role-scoped, no seating costs $ babuba agents add-user --agent finance --user asha --role user # edit at runtime — swap model, toggle tools, re-scope users $ babuba agents update --agent finance --model deepseek-v4-flash
No redeploys. Agents are runtime-editable: change the SOUL, swap the model, toggle tools, re-scope users — the gateway picks it up live.
Babuba ships governance built in — not bolted on. Four layers:
No. The gateway, agents, database and memory run on your machine. The only outbound calls are the LLM API calls the agents make — and with a local model, even those stay on your hardware.
Unlimited. One license covers as many agents as you want to create and as many users as you want to add. No per-agent or per-seat fees — ever.
Bring your own: OpenAI, Anthropic, DeepSeek, any OpenAI-compatible endpoint, or local models via Ollama. Swap models per agent at runtime.
support@babuba.ai — activation help, install walkthroughs and priority support for life. Get the license ▸