AI teammates for sales and customer success.
Scan accounts approaching renewal, surface usage and sentiment changes, and recommend next steps.
Build your AI teammate in 4 steps.
See how a revenue teammate turns customer signals into shared context, scheduled work, and human-approved action.
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 ↗“For each I own, watch , , and for churn signals on their . When a is near renewal and at risk, draft a save play and ping the who owns it before sending.”
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 ↗Acme Corp at risk
- Logins fell 38% over 14 days
- P1 #482 reopened on GitHub
- Renewal moved to Jun 30
Drafted a save-play email for Tony to send their CFO. Review before I hand it off:
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,
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 = defineBehavior({
agent: sales,
slug: "account-health-monitor",
name: "Account health monitor",
triggers: [{ kind: "schedule", cron: "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",
});
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],
behaviors: [accountHealthMonitor],
});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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