TanStack AI vs pi

pi is built for an agent that runs in one process: a terminal coding agent, a bot, or a script. TanStack AI does that too, and it also lets you split the agent across a client and a server. Pick pi if you want pi's coding agent CLI or its subscription logins. Pick TanStack AI for everything else, and for any app with a browser in it.

Both are open-source TypeScript toolkits under the MIT license. Both stream model output, call tools, run an agent loop, and talk to many providers. The difference is where the agent can run.

Where the agent runs

	pi	TanStack AI
In one process	Yes. agentLoop() runs next to the code that reads its events (agent.subscribe)	Yes. chat() returns an async stream you read in the same process. A harness keeps a session open across turns
In a terminal	pi-tui, or pi-coding-agent	runCli from @tanstack/ai-harness-cli: line mode, print mode for scripts and CI, NDJSON, and ACP for editors
Split across client and server	Not built in. pi-protocol and RPC mode can send events to another process. streamProxy keeps the loop in the browser and sends only the model call through your server	Built in. The loop and the server tools run on the server. The browser gets AG-UI events over HTTP and renders them with useChat
With a browser UI	Your own code	useChat for React, Solid, Vue, Svelte, Preact, Angular and more, with approvals, reconnects and saved history

So pi gives you an agent to embed in one process. TanStack AI gives you the same agent, plus a client and a server that talk to each other when you need them.

Pick TanStack AI when

You build a terminal agent, a bot, or a script. chat() runs the loop in your process. A harness keeps the conversation open, queues and steers messages, and runs typed agents from code. See Run a harness in the terminal.
You build a web app. useChat renders the run on the client, and the run survives a slow network or a reload. See UI and Resumable Streams.
Your tools must run on the server. Database calls, secrets and paid APIs stay on the server. The browser never sees them.
You want to define a tool once. One schema gives the input and output types on the server and on the client. Add .server() or .client() to run it on either side. See Tools.
You want people to approve tool calls. needsApproval pauses the run, and the user answers in the terminal or in the browser. See Tool approval.

TanStack AI also has these, each in its own package:

Coding-agent harnesses. Run Claude Code, Codex, OpenCode, Grok Build, or any ACP agent as a chat backend, in a local process or a sandbox. The harness is experimental. See Coding agents.
Subagents. A tool can run a child agent, and the client shows its run as a card. See Subagents.
Persistence. Writes to your database, in your schema. See Persistence.
Media beyond chat. Images, video, speech, transcription, music and realtime voice use the same adapters and persistence as chat. See Generations.

Pick pi when

You want pi's coding agent as the product. pi-coding-agent is a finished terminal agent that reads, edits and runs code in your repo. It saves sessions and loads extensions and skills.
You sign in with a model subscription. pi has OAuth logins for Claude Pro and Max, ChatGPT, GitHub Copilot and OpenRouter.
The loop must stay in the browser. streamProxy keeps the loop and the tools on the user's machine and sends only the model call through your server.

How the core APIs map

pi	TanStack AI
@earendil-works/pi-ai	@tanstack/ai and one adapter package per provider
createModels(), setProvider(anthropicProvider()), models.getModel('anthropic', id)	anthropicText(id) from @tanstack/ai-anthropic
models.streamSimple(model, context)	chat({ adapter, messages })
models.completeSimple(model, context)	chat({ adapter, messages, stream: false })
systemPrompt in the context	systemPrompts: ['...']
AgentTool with TypeBox parameters and execute	toolDefinition({ inputSchema }).server(fn)
agentLoop() or the Agent class	chat({ tools }), which runs the loop
beforeToolCall / afterToolCall	middleware: [...] with tool call hooks
agent.followUp(message)	The message queue of useChat
agent.subscribe() and AgentEvent	The AG-UI events of chat(), read by useChat on the client
streamProxy() in the browser	toServerSentEventsResponse on the server, useChat on the client
@earendil-works/pi-mcp	@tanstack/ai-mcp. See MCP.
@earendil-works/pi-codemode	@tanstack/ai-code-mode. See Code Mode.

Feature comparison

Versions: pi 1.4 and TanStack AI 1.0.

Feature	pi	TanStack AI
License	MIT	MIT
Main use	An agent in one process: coding agent CLI, and the libraries under it	Agents in one process, in a terminal, or split across client and server
Wire protocol	In-process AgentEvent objects. Remote sessions over CBOR (pi-protocol)	Native AG-UI, in both directions
Providers	About 35 in pi-ai, plus any OpenAI-compatible API	OpenAI, Anthropic, Gemini, Vertex, Bedrock, Grok, Groq, Mistral, OpenRouter, and more. Any OpenAI-compatible endpoint
Subscription logins	OAuth for Claude Pro and Max, ChatGPT, GitHub Copilot, OpenRouter (Node only)	API keys
UI	pi-tui for the terminal	Terminal (runCli), plus React, Solid, Vue, Svelte, Preact, Angular, Octane, Remix, React Native
Tools	AgentTool with a TypeBox schema, run in the agent	One toolDefinition(), with .server() and .client()
Tool approval	beforeToolCall can block a call	Per-tool needsApproval, with batched approvals and interrupts
Steering and follow-up	Built-in queues on the Agent	A message queue in useChat. The harness also steers a running turn (experimental)
Subagents	Task-owned child conversations in pi-durable (experimental)	Built in, with a card per child run
Structured output	Constrained sampling for tool arguments	Typed message part, streamed with tools and kept in the history
Token usage	Usage and cost on each assistant message	Usage on each model call, and cost when the provider reports it
Persistence	JSONL session files with branching. pi-durable is experimental	withPersistence middleware, server or browser stores
Resumable streams	pi-durable resumes a turn after a crash (experimental)	Built in. useChat reconnects by itself
MCP client	pi-mcp: stdio and Streamable HTTP	@tanstack/ai-mcp: multi-server pools, generated types, lazy tools, managed lifecycle
Code Mode	pi-codemode: QuickJS on Node, Bun and Cloudflare	6 isolate drivers: Node, QuickJS WASM, QuickJS Bun, Cloudflare, Daytona, E2B
Coding agents	pi-coding-agent, a CLI with extensions, skills and themes	Claude Code, Codex, OpenCode, Grok Build, and any ACP agent, on a local process or a sandbox
Image	Image generation through OpenRouter	Stable, typed per model
Video, speech, music	Not built in	Stable
Realtime voice	Not built in	Stable: OpenAI, Gemini, Grok, ElevenLabs