Build AI teammates that watch and act
Open-source infrastructure for AI teammates that watch, remember, and act. Connectors and webhooks build a live org knowledge graph; agents look it up and branch into a sandbox to do work.
Paste into your coding agent to scaffold a project.
Or start it yourself:
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, memory, and actions for one AI teammate.
Local, self-hosted, or managed.
Run on your laptop.
Boot the gateway, workers, memory, and embeddings with one command.
Run in your cloud.
Docker, cloud VM, or Kubernetes when data and controls need to stay with you.
Let Lobu run it.
Use the same project with managed isolation, secrets, and upgrades.
Build your first
multi-user agent.
Latest blog posts
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Shopify's Aquifer, in the Open
Shopify bet that an agent's corpus is the compounding asset. We made the same bet, with two differences: we keep the signal instead of the chat, and we built it for many companies instead of one.
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.