Spring AI Showcase — Parity Notes

This document tracks demos from the canonical langgraph-python showcase manifest that are not ported to the Spring AI showcase, along with the specific Spring AI / ag-ui:spring-ai primitive that is missing.

Spring AI is a Java framework with a narrower primitive set than LangGraph for a handful of specific use-cases — especially streaming structured output, multi-agent orchestration, and graph-level interrupts. The demos below are the ones where those primitives are genuinely unavailable.

Skipped demos

LangGraph graph-control primitives (no Spring AI equivalent)

• subagents — Ported using the tool-composition pattern (each sub-agent is a separate ChatClient call wired as a supervisor tool; see SubagentsController). This deviates from LangGraph's graph-as-node construct: there is no per-sub-agent interrupt point, and step-started/step-finished events are not emitted. The user-visible semantics — supervisor delegates work, each delegation is logged in shared state, the UI renders a live timeline — match the canonical demo. STATE_SNAPSHOT is emitted after every delegation so the delegation log updates incrementally.

ag-ui:spring-ai adapter gaps

• shared-state-streaming — Spring AI's ChatClient.stream() emits token deltas, but the ag-ui:spring-ai adapter does not expose a mid-stream state-delta emission API comparable to LangGraph's copilotkit_emit_state. Per-token state patches cannot be forwarded through the AG-UI channel with the current integration. The demo cell is shipped as a stub frontend (src/app/demos/shared-state-streaming/) so the UI lights up when the adapter exposes mid-stream emission.

• byoc-json-render — Relies on a streaming structured-output primitive (LangGraph's with_structured_output + incremental JSON streaming that yields partial objects matching a Zod schema across the stream). Spring AI has BeanOutputConverter / ParameterizedTypeReference structured output, but it resolves on the FINAL response only — it does not emit partial schema-conformant objects during the stream. The BYOC renderer needs per-token JSON to progressively paint the UI. Additionally, @json-render/core and @json-render/react are not currently dependencies of the Spring AI showcase package.

Ported with caveats

• gen-ui-interrupt — Ported using Strategy B (the same approach used by MS Agent Python). Spring AI has no interrupt() primitive, so the backend agent (InterruptAgentController) provides a scheduling system prompt with NO backend tool callbacks. The schedule_meeting tool is registered entirely on the frontend via useFrontendTool with an async handler that renders a TimePickerCard and blocks until the user picks a slot or cancels. The UX is identical to the LangGraph version.

• interrupt-headless — Same Strategy B adaptation as gen-ui-interrupt, but the time-picker popup renders in the app surface (outside the chat) instead of inline. Both demos share the same backend agent (InterruptAgentController).

• byoc-hashbrown — Ported. The hashbrown UI kit (@hashbrownai/[email protected]) consumes streaming text and uses useJsonParser to progressively assemble UI from partial JSON. Spring AI's ChatClient.stream() streams text tokens, so the hashbrown parser tolerates the per-token feed. Final-shape correctness depends on the model following the example prompt — there is no guarantee like LangGraph's with_structured_output.

• gen-ui-tool-based — Ported using useComponent per-tool renderers bound to render_bar_chart / render_pie_chart tools. Args stream as partial JSON; the Zod schemas accept partials so the chart components can render once enough fields are present.

• agentic-chat-reasoning, reasoning-default-render, tool-rendering-reasoning-chain — frontend code is wired for REASONING_MESSAGE_* events; when the adapter begins forwarding OpenAI reasoning content (and/or a reasoning-capable model is wired through), the reasoning UI lights up automatically. Until then the chat behaves as a regular chat. Shipped as frontend code so the pattern is documented end-to-end.

• multimodal — the frontend sends image + PDF attachments through CopilotChat's AttachmentsConfig. Whether the adapter forwards them into Spring AI's UserMessage.media() surface is integration-dependent; the Spring-AI model (gpt-4.1) is vision-capable on the provider side.

• mcp-apps — the runtime wires the MCP Apps middleware with the public Excalidraw MCP server. The middleware injects MCP tools into the AG-UI request so the Spring-AI ChatClient sees them, and intercepts tool calls to emit activity events. Whether the ag-ui:spring-ai adapter forwards runtime-injected tools into Spring AI's tool-calling surface is integration-dependent; the demo wiring is in place so the cell lights up when the adapter supports it.

Ported demos

The full ported list lives in manifest.yaml. Highlights include: agentic-chat, tool-rendering (default + custom + catchall), frontend-tools (+ async), hitl-in-chat (+ booking variant), hitl-in-app, prebuilt-sidebar / popup, chat-slots, chat-customization-css, headless-simple, headless-complete, beautiful-chat, auth, readonly-state-agent-context, open-gen-ui (+ advanced), voice, agent-config, a2ui-fixed-schema, declarative-gen-ui, multimodal, gen-ui-tool-based, mcp-apps, byoc-hashbrown, and the three reasoning variants.