What is Communitas?

Communitas is an AI community manager toolkit for people who take community health seriously. It helps community leaders, maintainers, and organizers see how their community actually works — who connects whom, where newcomers fall through the cracks, which clusters are drifting apart — and gives them research-grounded tools to do something about it. It is infrastructure for the humans already doing this work, not a replacement for them.

Four moves that define community work

Experienced community builders already cycle through four activities, whether they name them or not. Communitas turns each one into something measurable and improvable.

Understand what’s happening. Build a living picture of your community’s real interaction patterns. An open-source maintainer discovers that two active contributor groups have never reviewed each other’s code. A workplace team lead sees that engineering and design only interact in scheduled meetings — never organically.

Spot what’s breaking. Surface early warning signs before they become crises. A subreddit moderator notices the same three accounts appear in every heated thread. A project maintainer sees that newcomer activity drops sharply after day four. A civic group organizer realizes one volunteer is the sole connection between two working groups — a single point of failure.

Try something small. Suggest specific, low-risk interventions grounded in evidence. An opt-in introduction between contributors in different clusters. A personalized first-three-steps guide for a newcomer, matched with a mentor. A de-escalation prompt when a conversation starts running hot. Every suggestion is transparent, logged, and requires human approval.

Learn from what happened. Treat each intervention as a small experiment. Did the cross-cluster introductions lead to collaboration? Did the onboarding changes reduce newcomer drop-off? Update your playbooks based on outcomes, not assumptions.

A living model, not a static dashboard

Communitas builds a dynamic model of your community that updates as interactions happen. It tracks how relationships form and weaken, how clusters emerge and drift, where information flows freely and where it gets stuck. You can ask concrete questions: Where are we fragmented? Who bridges our clusters, and are they overloaded? How did last month’s changes affect newcomer retention?

The model focuses on what you can see and do — not on raw data schemas. It surfaces health patterns like bridge scarcity (too few people connecting different groups), newcomer drop-off, clique lock-in, and moderation hotspots, then connects those patterns to specific actions you can take.

Built on what researchers actually know

Communitas draws on a curated body of recent research (2020 to 2026) across network science, social computing, AI governance, and knowledge graphs. This research shapes every metric, experiment template, and intervention pattern in the toolkit. We treat the literature as a working knowledge base — not a bibliography to cite, but a foundation to build on and keep current. Read the research foundations.

Open research, private tools

The research, playbooks, and community metrics specifications are open. Anyone can use them to understand and improve their community. The private tools — living community graphs, health dashboards, agent deployments, governance tooling, and a research feed that tracks what’s new in the literature — are for operators who need more.

Why communities | Community graph model | Health metrics | Experiment registry | Get started with a pilot | Governance principles