Chat works the way you'd expect from any LLM client: a conversation thread, streaming responses, and full history. Each chat runs on an agent you choose, so you can switch between models and providers freely — there's no lock-in.
Organizing chats
Chats can be grouped into folders (which can nest), labelled, and searched. Titles are generated automatically from the first exchange, and a running token counter tracks input and output usage per conversation so you can see what a chat is costing.
Chatting inside a project
Attach a project to a chat and the assistant gains the same sandboxed filesystem tools a task gets — it can read, search, and edit files in the project directory and run commands. On Linux those calls are sandboxed. Without a project, the chat is plain conversation with no tools.
From chat to a Weave
With a project attached, you can ask the assistant to turn a request into a Weave: it uses a built-in tool to create the weave and its tasks, which you then review and run. You can also start a fresh chat seeded with a summary of the current one ("New from chat").
Per-chat options
- Agent — the model and provider this conversation runs on.
- Project — enables sandboxed tools scoped to its directory.
- MCP servers — toggle extra MCP tools on per chat.
- System prompt override and prompt caching can be set per conversation.