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.

• reasoning-custom, reasoning-default, tool-rendering-reasoning-chain — frontend code is wired for REASONING_MESSAGE_* events, but the Spring AI handler CANNOT emit them. This is a genuine SDK limitation in Spring AI 1.0.1, not an adapter or wiring gap. Details below.

  What the demo needs. The reasoning UI mounts only when the backend emits AG-UI REASONING_MESSAGE_START / _CONTENT / _END events (role "reasoning"). The canonical langgraph-python agent produces these by routing the OpenAI model's reasoning summary through the OpenAI Responses API (reasoning={"effort": "medium", "summary": "detailed"}). The aimock fixtures for these spring-ai cells (d6/spring-ai/reasoning.json, d6/spring-ai/tool-rendering-reasoning-chain.json, copied from langgraph-python) carry the reasoning text in a dedicated response.reasoning field, which aimock renders over the OpenAI chat-completions wire as streaming delta.reasoning_content chunks (see @copilotkit/aimock buildTextChunks — delta: { reasoning_content: slice }).

  Why Spring AI 1.0.1 cannot surface it. The spring-ai integration speaks OpenAI chat-completions (spring-ai-starter-model-openai, /v1/chat/completions). In spring-ai-openai:1.0.1 the streaming delta is bound to the record OpenAiApi.ChatCompletionMessage, whose components are exactly rawContent, role, name, toolCallId, toolCalls, refusal, audioOutput, annotations — there is no reasoning_content / reasoning field, no metadata map, and no @JsonAnySetter catch-all. The record is annotated @JsonIgnoreProperties, so the inbound reasoning_content JSON property is silently discarded at deserialization. It never reaches ChatResponse / Generation.getOutput(), so the Java handler has no API to read it. The reasoning-summary channel of the OpenAI Responses API is also unavailable: spring-ai-openai:1.0.1 ships no Responses-API client (only OpenAiApi chat-completions classes exist), so the langgraph-python parity path cannot be reproduced either.

  Why the inline-<reasoning>-tag workaround does not apply. The proven claude-sdk-python agent PRIMARILY maps Anthropic's native extended-thinking channel: it enables thinking={"type": "enabled", ...} on the Messages API, receives thinking_delta blocks, and re-routes them to REASONING_MESSAGE_*. Only when no native thinking channel is present does it FALL BACK to prompting the model to wrap its plan in literal <reasoning>...</reasoning> text tags inside normal output and parsing those tags out of the text stream. The inline-tag fallback IS expressible in Spring AI (the handler already streams getOutput().getText()). But neither claude-sdk path fits these cells: the spring-ai aimock fixtures emit reasoning through the dedicated reasoning field (→ reasoning_content), NOT via an Anthropic native thinking channel and NOT as inline <reasoning> tags in content. Rewriting the fixtures to embed inline tags — or hand-fabricating a reasoning block in the handler — would be a demo-weakening fixture hack that misrepresents the integration's real capability, so it is deliberately not done.

  What a real fix requires (upstream / out of scope here). Either (a) Spring AI adds a reasoning_content (or reasoning-summary) field to its chat-completions delta record and exposes it on Generation/output metadata; or (b) Spring AI ships an OpenAI Responses-API client that surfaces the reasoning summary; or (c) a custom WebClient-level interceptor parses the raw chat-completions SSE for delta.reasoning_content BEFORE Spring AI's binding drops it, bypassing ChatClient entirely (a substantial custom-parser effort that re-implements the streaming pipeline). None of these is a showcase-side change. Until one lands, these cells ship as frontend code (so the pattern is documented end-to-end) and the chat behaves as a regular chat with no reasoning block.

• 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.