Build proactive AI agents on a graph
that builds itself
Connect your company's data in real time, plug in your model, and let your agents act the moment something changes, as a bot, an API, or another agent.
Paste it into claude code, cursor, or opencode, and it scaffolds the project for you.
Or start it yourself:
Raw events in. Typed memory out. Agents act.
Connectors stream events into memory. Watchers derive typed entities. Agents read it, talk to users, and act.
One typed file wires it together.
Pick a piece to see the code for this use case. Click again to hide.
import {
connectorFromFile,
defineAgent,
defineConfig,
defineEntityType,
defineRelationshipType,
defineWatcher,
reactionFromFile,
secret,
} from "@lobu/cli/config";
import type SalesforcePipelineConnector from "./salesforce-pipeline.connector.ts";
import type accountHealthMonitorReaction from "./account-health-monitor.reaction.ts";
const sales = defineAgent({
id: "sales",
name: "sales",
description:
"Help revenue teams track account health, rollout progress, and renewal signals",
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 accountHealthMonitor = defineWatcher({
agent: sales,
slug: "account-health-monitor",
name: "Account health monitor",
schedule: "0 */12 * * *",
notification: { priority: "high", channel: "both" },
tags: ["sales", "health", "renewals"],
minCooldownSeconds: 1800,
reaction: reactionFromFile<typeof accountHealthMonitorReaction>(
"./account-health-monitor.reaction.ts"
),
prompt:
"Poll CRM data for tracked accounts. Track expansion progress, risk level changes, and renewal timeline.\n",
extractionSchema: {
type: "object",
required: [
"risk_level",
"expansion_status",
"renewal_blockers",
"activity_delta",
],
properties: {
risk_level: { type: "string" },
expansion_status: { type: "string" },
renewal_blockers: { type: "array", items: { type: "string" } },
activity_delta: { type: "string" },
},
},
});
export default defineConfig({
connectors: [
connectorFromFile<typeof SalesforcePipelineConnector>(
"./salesforce-pipeline.connector.ts"
),
],
org: "sales",
orgName: "Sales",
orgDescription:
"Help revenue teams track account health, rollout progress, and renewal signals",
agents: [sales],
entities: [organization, product, region, renewalRisk, team],
relationships: [affects, expandedInto, runs],
watchers: [accountHealthMonitor],
});Pick a use case to see it end to end.
Each page walks through the connectors, memory shape, and watchers for one team — and ships as a working example you can lobu apply.
Local, your cloud, or Lobu Cloud.
Same lobu.config.ts + *.connector.ts + agents/. One command to boot embedded; Docker + Helm for self-hosting; Lobu Cloud when you don't want to run it yourself.
Embedded, single process.
Gateway, workers, memory, embeddings, all in one Node process. Postgres is the only external.
$ lobu run → gateway :8787 → worker pid=<n> → memory N entities → watchers N armed
Docker. Helm. Your cloud.
Helm chart and Dockerfiles in the repo (charts/lobu/, docker/app/). Run on GCP, AWS, Fly, Render, or bare metal.
# Kubernetes $ helm install lobu ./charts/lobu # Docker $ docker build -f docker/app/Dockerfile .
Managed runtime.
Same code, run by Lobu. Per-user isolation, secret proxy, automatic upgrades.
$ lobu apply → org <your-org> → region <your-region> → agents N deployed → gateway <your-org>.lobu.run
Build your first
multi-user agent.
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