firstmate vs Lobu

firstmate and Lobu are both agent frameworks tracked by AIDiveForge. Below is a side-by-side comparison of pricing, capabilities, platforms, and ownership — sourced from each tool's live website and verified before publishing.

firstmate

firstmate puts a single orchestrating agent — the 'first mate' — in front of you, while it spawns a crew of autonomous coding agents behind the scenes, each isolated in its own git worktree. You describe what needs doing; the crew splits the work in parallel and keeps collisions out of your main branch. The visible session backend means you can watch what each agent is doing without switching tabs. The architecture works cleanly for investigation tasks, parallel fixes, or supervised PR generation — the constraint is that there is no API surface, so anything requiring programmatic integration into an existing CI pipeline has to wire around the tool manually.

Lobu

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.

Attribute	firstmate	Lobu
Pricing	Free	Paid
Free trial	No	No
Open source	Yes	Yes
Has API	No	Yes
Self-hosted option	Yes	Yes
Platforms	—	Local, Docker, Kubernetes, Lobu Cloud
Pros	Crew-based parallel dispatch, so three investigation or fix tasks run simultaneously instead of sequentially — cutting the wall-clock time you'd spend babysitting separate agent sessions. Per-agent git worktree isolation, which means parallel agents working on adjacent code do not produce mid-run merge conflicts that you have to untangle before any output is usable. Visible session backend for the whole crew, so you can monitor what each agent is doing without switching terminals or losing track of which session held the failing test. Self-hosted under MIT license with no paid features gated behind a tier, so teams with data-residency or audit requirements can deploy it without a vendor conversation. Supervised agent loops with PR or report output as the end state, which means you review finished work rather than raw agent traces — keeping you in the loop at the decision point that matters.	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	No API surface exists in the architecture, so teams that need to trigger agent crews from a CI system or external scheduler have to build shell-level integrations against a tool not designed for that pattern — and maintain that glue code themselves. The crew model requires a human interacting with the first mate agent as the starting point; fully unattended, scheduled agent runs with no human in the dispatch loop are not a supported workflow, which is the condition under which teams move to an orchestration framework that exposes a programmatic entry point. Community support through GitHub issues is the primary support channel — with 29 open issues noted on the repo — so teams encountering edge-case failures in production have no escalation path beyond the open-source community.	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.

Bottom line

Firstmate is free while Lobu is paid; only Lobu exposes a public API. Choose based on which difference matters most for your workflow.

Frequently asked questions

What is the difference between firstmate and Lobu?

firstmate is Free and open source, while Lobu is Paid and open source. Compare pricing, free trial, API, platforms, and pros/cons in the table above on AIDiveForge.

Is firstmate better than Lobu?

It depends on your workflow. Use the side-by-side attributes (pricing, open source, API, self-hosted, platforms) to decide. AIDiveForge does not rank a universal winner — we publish verified facts so you can choose.