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June 10, 2025

Multi-user AI chat with RedwoodSDK RSC and Cloudflare agents

At a recent event in London, I was excited to learn how RedwoodSDK makes it easy to run React Server Components (RSC) on Cloudflare.

I decided to learn more about this by building multi-user AI chat. Code on GitHub. Deployed at https://agents-chat.jldec.me/.

Here are just a few of the challenges:

  • Live-streaming AI responses back to multiple connected clients
  • Subagent creation, lifecycle, identity, sub-threads, approvals
  • Conversation persistence, re-use, summaries
  • Tool discovery, selective use, approval flow
  • User identity, auth, authz
  • Long running tasks
  • Notifications

First impressions

  • RedwoodSDK (RSCs on Cloudflare workers) is very interesting. The addition of client-side navigation (SPA mode) together with Cloudflare cache integration, will make this stack hard to beat.

  • I was a bit surprised to learn that RSC pre-renders use client components on the server, on initial load. This can produce errors e.g. running the useChatAgent() hook from the agents sdk. The rwsdk team was super-responsive helping to debug the issue, and provided a solution to disable ssr. More details in this PR.

  • All implementations rely on Cloudflare durable objects with websockets. This is great for runtime performance and makes deployment easy. There are no containers to build or servers to manage.

  • React is great for a use case like this where updates are coming from both the server and the client. All implementations use the same MessageList component.

RedwoodSK realtime RSC

  • Server components are a succinct way to pre-populate JSX with data and then keep clients up to date.

  • It’s nice to be able to use async data loading inline on the server. Rendering with data from remote storage during streaming can be slower unless data is memoized.

  • The scope of the RSC update payload sent to clients may become a problem during streaming, e.g. for pages with a lot of data. Discussion about this in the rwsdk discord.

  • Server functions are convenient, but should be used with care since they generate HTTP APIs which is where auth/authz is commonly required. See this take from Jack Herrington for more.

Cloudflare Agents raw websockets

  • Using Cloudflare Agents raw websockets gives us full control over the payloads. This allows for nice optimizations e.g. to send partial data during streaming.

  • Rendering chat history on the client via fetch or via websocket makes the initial UX a little janky. (TODO: investigate pre-rendering)

Cloudflare Agents SDK with AIChatAgent

  • AIChatAgent handles multi-user real-time message sync over websockets. This simplifies the implementation.

  • The SDK abstracts tool calling and supports different LLMs with Vercel’s AI SDK.

  • useAgentChat which calls AI SDK’s useChat, manages chat UI interactions with react, however only a single client sees the AI streaming reponse (see #23).

Agent Agent with subagents and MCP tools

  • MCP tools can be added, removed or listed.

  • A built-in tool calls AIChatAgent.saveMessages() on a named subagent, passing in a new message, as if it were coming from a user. This makes it possible for the main agent to prompt the subagent. Subagent responses currently don’t stream (TODO)

OpenAI Agents SDK

  • OpenAI Agents SDK is less mature and focuses on model APIs, not UI integration.

  • It offers APIs for realtime, handoffs, and subagents - not used in this project yet.

  • The stateful Agent abstraction assumes long-running server processes with a single conversation per agent.

TinyBase sync

  • Synchronization is happening between memory and persistance on every node, and between nodes.

  • This improves the UX once data is persisted on the client and makes it easy to use React hooks listening for database updates.

  • The APIs for persistence and synchronization feel like they could be consolidated.

  • Since store operations run on the client we have to be extra careful with validation e.g. to deal with clients being compromized.

What’s next?

I’d ❤️ to see if there are ways to combine the best of all the approaches above.

And I believe multi-user AI chat could be the beginning of something much bigger.

  • Agents and humans organize into specialized groups.
  • Universal agents with tools do much of the work.
  • Persisted conversations become the new basis for organizational memory.

Bright futures ahead 🚀