AI teammates for leadership operations.
Summarize new board memos: what was approved, what is blocked, and who owns each next action.
From source changes to shared memory.
Connect the systems this teammate can read, define its goal, and keep every result grounded in current, scoped context.
Connect your data
Pick the systems it can read. Lobu turns those updates into live customer memory.
Define the goal
Tell it what to watch for and when to ask before acting.
“Summarize new board memos: what was approved, what is blocked, and who owns each next action.”
Lobu works autonomously
It scans memory on schedule, spots the account at risk, and keeps the evidence attached.
You review and approve
You can edit the draft, send it, or leave it.
The whole agent, in code.
One project defines it end to end: the agent, its connectors, the memory schema, watchers, and skills. Write it yourself, or let your coding agent generate it. Pick a piece to read the code.
import {
connectorFromFile,
defineAgent,
defineConfig,
defineEntityType,
defineRelationshipType,
defineWatcher,
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 = defineWatcher({
agent: leadership,
slug: "board-action-tracker",
name: "Board action tracker",
schedule: "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],
watchers: [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.
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.
Latest blog posts
The Agent Loop Is the New SaaS
The business logic SaaS sold you is now an agent loop that watches your data and acts. Here is how to build and own one with Lobu.
Read post →Shopify's Aquifer, in the Open
Shopify treats an agent's corpus as the compounding asset. Lobu keeps the signal—not the chat—and makes that architecture work across companies.
Read post →Filesystem vs Database for Agent Memory
Agents need a workspace to think in and a warehouse to remember in. The filesystem is for ephemeral work. The memory layer is for durable organizational knowledge.
Read post →Give every teammate a colleague.
Paste the setup prompt into Claude Code — lobu.config.ts, role files, and channels land in your repo. Ship it today.