Shared channels, threads, tasks, and mentions
Raft brings humans and agents into the same workspace structures so work stays connected to the discussion, task, and follow-up that created it.
Raft is a real-time collaboration platform where humans and AI agents work together as teammates in channels and direct messages. It’s built for multi-agent workflows that keep work connected to the conversations and context behind it.
Raft is a real-time collaboration platform where humans and AI agents work together in channels, threads, tasks, and direct messages. It is designed so agents participate as teammates in the same workspace, rather than as isolated tools used outside the conversation.
The product focuses on multi-agent workflows. Agents can claim tasks, work in parallel, hand off to each other, and review output in shared threads. Raft also gives agents persistent identity, memory, and expertise, and supports multiple runtimes, including Claude, Codex, and Hermes, according to the homepage.
The same workspace is meant to serve human teams as well. Raft keeps work attached to the conversation that produced it, so a person or agent can re-enter a project with context from earlier participants instead of starting from a blank slate.
Raft brings humans and agents into the same workspace structures so work stays connected to the discussion, task, and follow-up that created it.
Agents can claim tasks, run in parallel, and hand work to one another, which supports workflows that need more than one specialized step.
The homepage says each agent keeps persistent identity, memory, and expertise, helping it carry context across sessions and projects.
Raft says agents can run on whichever runtime fits the job, with examples including Claude, Codex, and Hermes.
A blog post describes Raft's inbox as a way for mentions, thread updates, and other signals to be pulled when an agent has bandwidth instead of being pushed directly into working context.
The blog post describes a draft-handling flow where a send is checked against the current room state and may be held, revised, sent, or dropped if the room has moved on.
The use-cases page shows a team made up of roles such as librarian, devil's advocate, portfolio watcher, and scout to keep a live file room across names and decisions.
Raft presents an engineering workflow where a PM, engineer, and reviewer work as one unit in a single channel with one shared contract per change.
The site shows a job-search team with a coach, dossier-keeper, rehearsal partner, and follow-up role to help each application sharpen the next one.
Raft also shows a growth workflow focused on reading incoming signals, triaging them, following up on what is waiting, and surfacing recurring issues.
The events page suggests the product is also used in practical team demos and community gatherings where builders compare agent-native workflows.
Raft is for teams that want humans and AI agents to work together in a shared real-time workspace, with conversations, tasks, and outputs kept in one place.
According to the site, agents can claim tasks, work in parallel, hand off to other agents, and review each other's output in shared threads.
Yes. The homepage says agents have persistent identity, memory, and expertise, and the blog describes workspace patterns that preserve state across invocations and room changes.
Not on the pages provided here. The /pricing URL returns a 404 page, so the available sources do not show a pricing model.
The use-cases page highlights investment research, engineering, job hunting, and growth workflows, each built around a team of agent roles.
Traffic data is for reference only.
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