Adapt
Lets a company build one AI agent that lives inside Slack, GitHub, Linear and other tools it already uses, instead of juggling separate AI features bolted onto each app.
🔗 Visit Adapt
Description
Most companies now have AI scattered everywhere — a chatbot in the helpdesk tool, an assistant inside the code editor, a summarizer in the meeting app — each one separate, each one forgetting what the others know. Adapt's pitch is to replace that patchwork with a single "universal agent" that lives wherever your team already works: it can answer questions in a Slack thread using live company data, triage and review pull requests inside GitHub, and automate internal workflows, all as one consistent agent rather than five disconnected ones.
Technically, it's built as three layers: the apps layer (integrations across Slack, GitHub, Linear, web, Messages and Teams), a model router that sends each request to whichever underlying AI model fits best — with automatic failover mid-conversation if one provider has an outage — and an infrastructure layer of microVM sandboxes with persistent file systems and cron-based automation for tasks that need to run on a schedule. It's deployable self-hosted, in your own cloud (VPC), or through Adapt's managed cloud, and is SOC 2 Type II certified with a DPA available, aiming squarely at companies that need enterprise-grade data governance around their AI usage.
💬 Our review
The short version: it's aimed at companies that want one AI agent embedded across their actual tools rather than five separate AI add-ons, with enterprise security credentials to back up deployment inside a real company's infrastructure.
Against open frameworks like LangChain or LangGraph, Adapt isn't a library you assemble yourself — it's closer to a managed platform with the integrations and infrastructure already built, which trades flexibility for speed of deployment. That positions it more against enterprise AI platform vendors than against a DIY agent stack, and the lack of public pricing (demo-only) confirms that positioning — this isn't a self-serve tool for a five-person startup. The model-router-with-failover design is a genuinely useful piece of engineering if you're worried about single-provider outages taking down an agent your team now depends on. Worth evaluating if you're a mid-size or larger company standardizing AI across several tools at once; overkill for a single-tool AI integration or a small team.
💰 Pricing
📊 Global score
🤖 AI-enriched data
Pas de tarification publique, démo sur demande
Pros
Un agent unique multi-outils
Routage multi-modèles avec failover
Certifié SOC 2 Type II
Cons
Pas de prix public
Intégration Teams pas encore live
Positionnement entreprise uniquement