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Lobu
Summary
The agent you built works fine for one user watching one account — then a second team member joins, a second data source goes stale, and suddenly you're the glue layer your agent was supposed to replace. Lobu is built for that exact moment: shared memory, scheduled watching, and approval steps that let agents act across teams without going rogue.
Lobu connects to over 50 data sources — HubSpot, Stripe, Zendesk, Snowflake, GitHub, and more — and builds a live memory layer that agents query on schedule rather than on demand. A 'watcher' definition tells the agent what to look for and when to pause for a human to sign off before anything ships. That approval-before-action model is what makes the autonomous scanning safe enough to actually run unsupervised. The ceiling shows up when your workflow needs logic that doesn't fit a watcher definition — at that point you're writing connector SDK code and maintaining it yourself. Teams with deeply custom data pipelines will feel that constraint before teams running standard SaaS stacks.
Bottom line: Lobu earns its place when you need multiple agents sharing memory across a sales, legal, or finance team with scheduled scans and approval gates — but if your workflow demands branching conditional logic beyond watcher definitions, you'll be writing and maintaining custom connector code on top of it.
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Pros
Sign in to edit- Persistent shared memory across all connected sources, so multiple team members querying the same agent see consistent, evidence-backed context rather than each starting from a fresh prompt.
- Approval-before-action steps baked into watcher definitions, so agents can run unsupervised on a schedule without the risk of sending a customer-facing message or filing a report without a human signing off first.
- Over 50 pre-built connectors plus a Connector SDK for arbitrary data sources, so teams with non-standard stacks aren't blocked waiting for a native integration.
- Three deployment modes — local CLI, Docker/Kubernetes self-hosted, and managed cloud — using the same project config, so a team can prototype on a laptop and promote to their cloud without rewriting the agent definition.
- Open-source codebase with 13 public example workflows covering sales, legal, finance, and market research, so teams inherit tested patterns rather than building agent memory architectures from first principles.
Cons
Sign in to edit- Watcher definitions are goal-and-approval constructs, not branching pipelines — there is no built-in way to say 'if the contract risk is high, route to legal; if medium, route to the account owner.' Teams that need that decision tree write it in the Connector SDK, which means owning and testing a custom code layer alongside the Lobu config.
- Teams whose core requirement is conditional routing between multiple agents — not monitoring and drafting, but complex multi-step task pipelines — will hit the watcher model's ceiling early and migrate to a dedicated agent orchestration framework. The memory and connector infrastructure doesn't transfer; the switch is a full rebuild.
- The managed cloud offering is a paid-only feature with no pricing details published on the vendor page, so teams trying to size budget before committing to a proof of concept must contact the vendor directly — a friction point that slows evaluation for organizations that require procurement approval before a pilot.
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About
- Platforms
- Local, Docker, Kubernetes, Lobu Cloud
- API Available
- Yes
- Self-Hosted
- Yes
- Last Updated
- 2026-06-20T02:18:28.016Z
Best For
Who it's for
- Teams building multi-user autonomous agents
- Organizations needing memory and connector management for agents
- Users wanting human-in-the-loop approval for agent actions
- Self-hosted or cloud deployments with API access
What it does well
- Track account health and renewal signals for sales teams
- Review contracts and surface risks for legal workflows
- Reconcile data and prepare reports for finance
- Monitor companies and draft introductions for market research
- Maintain shared organizational memory across tools
Integrations
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Frequently Asked Questions
- Is Lobu free?
- Lobu is a paid tool. No permanent free tier is offered.
- Is Lobu open source?
- Yes. Lobu is open source.
- Does Lobu have an API?
- Yes. Lobu exposes a developer API. See the official documentation at https://lobu.ai for details.
- Can I self-host Lobu?
- Yes. Lobu supports self-hosting on your own infrastructure.
- What platforms does Lobu support?
- Lobu is available on: Local, Docker, Kubernetes, Lobu Cloud.
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Curated lists that include this category
Agents that scrape a snapshot and forget it are useless for account health, contract review, or variance analysis — the signal lives in the delta between yesterday and today. Lobu addresses this by maintaining a persistent, structured memory layer tied to live connectors, so agents scan for change rather than reprocess everything from scratch. The core workflow is four steps: connect data sources, define a watcher goal with an approval condition, let the agent scan on schedule and attach evidence to memory entities, then review and act on what surfaces in your team’s chat or via the API.
The differentiating design choice is the watcher-plus-approval model. Instead of agents that fire actions automatically, Lobu lets you specify exactly when the agent must pause and surface a draft for a human to edit, send, or discard. The vendor describes this as the agent ‘keeping evidence attached’ — meaning the reasoning behind a churn flag or contract risk isn’t buried in a log, it’s stored alongside the memory entity so the person reviewing it can audit the signal before acting.
Lobu fits teams building multi-user agents where shared organizational memory matters: a revenue agent that multiple CSMs query, a legal agent that routes contract risk to the right reviewer, a finance agent that prepares variance reports on a schedule. It runs locally via a single CLI command, self-hosted via Docker or Helm for teams where data cannot leave their cloud, or as a managed cloud offering. The open-source codebase and 13 public example workflows on GitHub give teams a concrete starting point rather than a blank canvas.
Where it breaks: watcher definitions are goal-oriented and work well for monitoring and drafting workflows, but the docs describe no built-in visual branching or conditional routing between agents. Teams that need one agent’s output to trigger a different agent down a decision tree will find themselves writing that logic in the connector SDK — a separate code layer that needs its own testing and maintenance. At that point the simplicity of the watcher model is mostly gone, and teams running complex multi-step orchestration often evaluate purpose-built agent frameworks instead.