Build AI teammates that watch and act
Lobu connects your tools, keeps shared memory current, and gives agents safe ways to act across chat, APIs, CLI, and MCP.
Paste into your coding agent to scaffold a project.
Or start it yourself:
Catch what you'd miss.
Connect your data. Set a goal. Lobu watches for changes and prepares the next step.
See sourceConnect 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.
“Watch every account for churn risk. If renewal is within 30 days and health drops, draft a CSM check-in for approval.”
Your agent 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.
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.
Deploy with Docker or Helm 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
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.
MCP Is Overengineered, Skills Are Too Primitive
MCP HTTP is great for external services. MCP stdio is redundant. And most skill systems are just prompt text with no reproducibility. Here's what we built instead.