AI teammates for leadership operations.
Summarize new board memos: what was approved, what is blocked, and who owns each next action.
Build your AI teammate in 4 steps.
See how a leadership teammate connects initiatives, decisions, owners, and follow-through across the organization.
Connect your systems.
Connectors stream your tools into memory. Models, sandboxes, and devices are independent layers — text, image, speech, and more, credentials gateway-side. Flip the tabs to wire each one.
Connectors ↗Model your business.
Declare the people, business objects, and relationships your agent needs. Every fact from the connectors above converges on the right record, so any teammate reads the same picture.
Memory ↗Define the work.
A trigger — a schedule, or reactive to connector events — plus a plain-language prompt. Once defined it runs unprompted: scans memory, matches the trigger, and surfaces the work with the evidence attached.
Behaviors ↗“On every new , watch and for follow-ups on its . When an is blocked, flag it and ping the who owns the before the next review.”
Talk to your teammate everywhere.
Delegate in chat — Slack, Teams, WhatsApp, the API, or MCP. The agent proposes with buttons; you review, edit, and approve. Approving triggers a run that hands the work off to the right person.
Platforms ↗Action needed · Board Q4
- $4M Series A bridge approved
- Q1 hiring freeze reaffirmed
- Frankfurt lease counter due Apr 25
Drafted the exec digest with owners. Review before I post it:
A framework for AI teammates you own.
Not hand-rolled scripts. Not a rented employee. An open-source backend — connectors, shared memory, sandboxed execution, and observability in one — for teammates that watch, remember, and act. Yours to self-host and scope per person. See how it compares →
Everything an agent runs on.
Four swappable runtime layers — inference, execution, memory, and connectors — with evals, deployment, and observability built in. Replace any runtime layer without touching the others.
The same agent, in code.
The use case above is one project: connections, entity types, behaviors, and agent configuration. Inspect each piece or let your coding agent generate it.
import {
connectorFromFile,
defineAgent,
defineConfig,
defineEntityType,
defineRelationshipType,
defineBehavior,
secret,
} from "@lobu/cli/config";
import type LinearCyclesConnector from "./linear-cycles.connector.ts";
const leadership = defineAgent({
id: "leadership",
name: "leadership",
description:
"Help leadership teams turn memos, decisions, and board materials into reusable operating context",
dir: ".",
providers: [
{
id: "anthropic",
model: "claude/sonnet-4-5",
key: secret("ANTHROPIC_API_KEY"),
},
],
network: {
allowed: [
"github.com",
".github.com",
".githubusercontent.com",
"registry.npmjs.org",
".npmjs.org",
],
},
});
// entity types and relationships defined here…
const boardActionTracker = defineBehavior({
agent: leadership,
slug: "board-action-tracker",
name: "Board action tracker",
triggers: [{ kind: "schedule", cron: "0 8 * * *" }],
notification: { priority: "high", channel: "both" },
tags: ["leadership", "daily", "board"],
agentKind: "notifier",
prompt:
"Track board action items: check task delivery status, blocker resolution progress, and approaching deadlines for the next board packet.\n",
});
export default defineConfig({
connectors: [
connectorFromFile<typeof LinearCyclesConnector>(
"./linear-cycles.connector.ts"
),
],
org: "leadership",
orgName: "Leadership",
orgDescription:
"Turn memos, decisions, and board materials into reusable operating context",
agents: [leadership],
entities: [decision, document, region, risk, task],
relationships: [approved, assigned, blockedBy],
behaviors: [boardActionTracker],
});Explore agent workflows.
Each example shows the sources, the memory, and the action for one AI teammate. Same loop (connect, watch, act) pointed at a different job.
Tracks account health, rollout progress, and renewal signals across the customer base.
Reviews incoming contracts, summarizes risk, and surfaces missing protections before sign-off.
Reconciles data across systems, explains variance, and prepares recurring reporting runs.
Declarative. Yours. In your repo.
Every teammate above is a lobu.config.ts — desired state you version, review, and own.lobu validate checks it, lobu applyships it, lobu run boots it. No magic, no lock-in.
Local, self-hosted, or managed.
The same project runs on your laptop, in your cloud, or fully managed by lobu. Your data and controls stay wherever you need them.
Run in your cloud.
Docker, a cloud VM, or Kubernetes when data and controls need to stay with you.
Let lobu run it.
The same project with managed isolation, secrets, and upgrades, nothing to operate.
Questions a technical buyer asks first.
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