Track what your team and agents know. Handoff is a real-time AI workspace that syncs with your tools. Always current. Built for human-agent collaboration.

Want to check it out? It's live for beta testing! 👉 https://handoff.computer

The Problem

AI is generating more knowledge than ever. And we're losing it faster than ever.

Every conversation with an AI produces insights, decisions, and context. But when the chat ends, it's gone. Locked in proprietary “memory” systems you can't inspect. Scattered across a dozen tools. Impossible to share with your team or your other agents.

The irony is stark: we have the most powerful knowledge tools in human history, and we're still copying and pasting between them. Still re-explaining the same context in every new session. Still watching valuable insights evaporate into the void.

Most AI memory is a black box. You put things in, you hope the right things come out. You can't see what's there, edit what's wrong, or share what's valuable. That's not memory. That's a lottery.

The Solution

Durable context for shared understanding. No more stale context. Handoff keeps your knowledge current and delivers exactly what each person and agent needs.

• Capture - Save knowledge, notes, and sources as structured items
• Organize - Connect items into graphs that reflect how ideas relate
• Track - Version history keeps everything auditable and reversible
• Share - Expose context to agents and teammates via MCP in real-time

Agent‑First Model

Handoff is the context middleware between your sources and your AI work environments. It’s agent‑first by design, so your structure remains stable as models and hosts evolve.

Core Concepts

Spaces group-related work; each has a root item. Items can be Empty (structure‑only) or Text (markdown). Either way, they can carry structured fields (stored in props.fields) and children. Edges connect items to form sub‑graphs you can reuse.

How it works

Handoff sits between your sources and your agents as the context layer, memory playground, and control plane. It works with your own agents using the Model Context Protocol (MCP) as the backplane-a deliberate choice so Handoff provides real value with your existing tools. Anything you do in the app you can also do via your own MCP client.

Use Cases

Below are concrete use cases Handoff supports. See Context Retrieval for the three ways to get this context into your AI.

• Memory for agents: assemble items and links to dynamically share context during agentic runs; capture and reuse results.
• Structured docs & note taking: organize living notes, outlines, and pages as Items (Empty/Text); link with contains edges.
• Collections & lists: track tasks, references, bookmarks, or datasets as Items; group with contains edges.
• Research graphing: capture notes and papers as Items; relate authors, topics, findings; make it available for agents.

Experimental

• Agent buildroom (multi‑agent orchestration): coordinate multiple agents over long runs, sharing evolving context via Handoff; capture and link results for reuse.
• Interactive runbooks: encode steps as Items with ordered edges; let the agent read/track progress.
• Design system memory: track tokens/components as Items; generate consistent UI code/content.

Agent Presence

Presence lets your UI follow along with actions your own agents take - selecting items and switching spaces automatically when appropriate.

How it works

When agents create/update items or spaces, the API emits lightweight presence hints (e.g., “select item”, “select space”). The web app listens for these hints and, when enabled, auto-selects the targeted item or navigates to the space. Presence events only apply to actions from your own user/agents - never other users.

Initial Design