Due Diligence Agents vs Floatboat

Due Diligence Agents and Floatboat are both ai agent apps 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.

Due Diligence Agents

The tool runs parallel analysis across Legal, Finance, Commercial, Technology, Cybersecurity, HR, Tax, Regulatory, and ESG workstreams — domains that siloed consultants hand off sequentially, bleeding weeks in the process. Each agent cross-references findings against the others, so a revenue concentration risk in the commercial workstream gets flagged against the indemnification language in legal without a human manually connecting the dots. Outputs land in Excel and Word with citations intact, ready for an IC memo. The knowledge compounds across deal runs, so repeat buyers in the same sector start with context the first team had to build from scratch. The ceiling appears when your data room contains formats the parser does not handle cleanly — and at that point, teams are pre-processing documents manually before the agents ever see them.

Floatboat

The core premise: each calendar block fires an agent rather than booking a meeting. Floatboat reads upcoming events, runs pre-configured Combo Skills beforehand — turning voice notes into decks or Linear tickets into PR drafts — and deposits finished artifacts into Notion or your inbox before you open the app. Persistent Agent Workspaces carry files, run history, and model choice across Mac, Windows, and teammates via FloatIM group chat. The ceiling appears when your workflow needs logic that departs from calendar triggers — ad-hoc branching, multi-condition routing, or deeply custom pipelines demand workarounds. No API is available, so teams that want to embed Floatboat's execution engine into an existing product hit a hard wall.

Attribute	Due Diligence Agents	Floatboat
Pricing	Free	Paid
Free trial	No	No
Open source	Yes	No
Has API	No	No
Self-hosted option	Yes	No
Platforms	Python (Linux, macOS, Windows via Docker or local install)	Mac, Windows
Pros	13 agents analyze nine domains in parallel rather than sequentially, which means a workstream that would take a consultant team weeks to hand off completes in a fraction of the calendar time. Every finding is traced to an exact page and quote in the source document, so IC memos and advisor reports arrive with citations pre-built rather than requiring a second pass to source claims. Cross-domain synthesis flags when a finding in one workstream changes the risk weight of a finding in another — catching the legal exposure a pure financial review would miss. Knowledge compounds across deal runs, so teams analyzing targets in a recurring sector carry prior context forward instead of rebuilding domain understanding from zero each time. Self-hostable under Apache-2.0, which means data room documents stay inside the team's own infrastructure rather than transiting a third-party SaaS layer — a requirement many corporate legal and compliance functions enforce.	Calendar-driven execution means prep briefs and post-meeting follow-ups fire automatically, so you stop losing the hour before every standup to manual context-gathering. Persistent Agent Workspaces carry run history, files, and model choice across sessions and devices, which means context does not reset between Monday and Friday — the problem that makes session-based chat tools feel like amnesia. Auto Mode routes each Combo step to the cheapest sufficient model and fails over instantly when a provider rate-limits, so a multi-step run completes without you babysitting it. FloatIM's local-first group chat keeps agent execution on-device by default, so teams handling confidential files avoid routing sensitive data through a cloud intermediary. Pre-built Combo Skills install in one click and run on calendar triggers or file drops, delivering artifacts to Notion or your inbox before you open the app — which means the output is waiting for you, not the other way around.
Cons	Non-standard document formats — scanned PDFs without clean OCR, nested Excel models, heavily formatted legal exhibits — require manual pre-processing before the agents can operate on them; on data rooms where half the documents need cleaning, the time compression the tool promises shrinks significantly. The tool has no API surface, so teams that want to trigger analysis from an existing deal management system or integrate outputs into a live workflow dashboard cannot do so without forking the codebase and building the integration themselves. The external LLM dependency means cost and latency are governed by whichever provider the team configures — a large data room routed through a rate-limited API will queue, and teams running multiple deals in parallel against the same LLM endpoint will feel that ceiling; at that point, teams with the infrastructure budget move to a dedicated model deployment rather than a shared API.	Workflow logic that lives outside calendar triggers — ad-hoc branching, multi-condition routing, or pipelines kicked off by a webhook rather than an event — has no documented execution path in Floatboat; teams with those requirements build around it in a separate tool or switch to a general-purpose agent framework. No API is available, so any team that wants to embed Floatboat's agent execution inside an existing product or data pipeline hits a hard stop; at that point the architecture conversation moves to tools like n8n or a self-hostable LLM framework. The Freemium model gates commercial-grade features, and the boundary between what is free and what is paid-only is not explicit in the public docs — teams scoping production use before committing discover this ceiling after onboarding, not before. FloatIM's agent-to-agent coordination is local-first by design, which is a privacy advantage but means real-time multi-user collaboration across larger teams requires explicit sync decisions; studios scaling past five people report the model strains before a proper team tier is clear.

Bottom line

Due Diligence Agents is free while Floatboat is paid; Due Diligence Agents is open source; only Due Diligence Agents can be self-hosted; Due Diligence Agents runs on Python (Linux, macOS, Windows via Docker or local install); Floatboat on Mac, Windows. Pick the difference that actually blocks you.

Frequently asked questions

What is the difference between Due Diligence Agents and Floatboat?

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

Is Due Diligence Agents better than Floatboat?

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.