wehub-resource-sync
166 lines
9.1 KiB
Markdown
166 lines
9.1 KiB
Markdown
# Spring AI Showcase — Parity Notes
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This document tracks demos from the canonical `langgraph-python` showcase
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manifest that are **not ported** to the Spring AI showcase, along with the
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specific Spring AI / `ag-ui:spring-ai` primitive that is missing.
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Spring AI is a Java framework with a narrower primitive set than LangGraph
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for a handful of specific use-cases — especially streaming structured
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output, multi-agent orchestration, and graph-level interrupts. The demos
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below are the ones where those primitives are genuinely unavailable.
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## Skipped demos
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### LangGraph graph-control primitives (no Spring AI equivalent)
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- **subagents** — Ported using the tool-composition pattern (each
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sub-agent is a separate `ChatClient` call wired as a supervisor tool;
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see `SubagentsController`). This deviates from LangGraph's
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graph-as-node construct: there is no per-sub-agent interrupt point, and
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step-started/step-finished events are not emitted. The user-visible
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semantics — supervisor delegates work, each delegation is logged in
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shared state, the UI renders a live timeline — match the canonical
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demo. STATE_SNAPSHOT is emitted after every delegation so the
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delegation log updates incrementally.
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### `ag-ui:spring-ai` adapter gaps
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- **shared-state-streaming** — Spring AI's `ChatClient.stream()` emits
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token deltas, but the `ag-ui:spring-ai` adapter does not expose a
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mid-stream state-delta emission API comparable to LangGraph's
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`copilotkit_emit_state`. Per-token state patches cannot be forwarded
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through the AG-UI channel with the current integration. The demo cell
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is shipped as a stub frontend (`src/app/demos/shared-state-streaming/`)
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so the UI lights up when the adapter exposes mid-stream emission.
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- **byoc-json-render** — Relies on a streaming structured-output primitive
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(LangGraph's `with_structured_output` + incremental JSON streaming that
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yields partial objects matching a Zod schema across the stream). Spring
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AI has `BeanOutputConverter` / `ParameterizedTypeReference` structured
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output, but it resolves on the FINAL response only — it does not emit
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partial schema-conformant objects during the stream. The BYOC renderer
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needs per-token JSON to progressively paint the UI. Additionally,
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`@json-render/core` and `@json-render/react` are not currently
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dependencies of the Spring AI showcase package.
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## Ported with caveats
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- **gen-ui-interrupt** — Ported using **Strategy B** (the same approach
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used by MS Agent Python). Spring AI has no `interrupt()` primitive, so
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the backend agent (`InterruptAgentController`) provides a scheduling
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system prompt with NO backend tool callbacks. The `schedule_meeting`
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tool is registered entirely on the frontend via `useFrontendTool` with
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an async handler that renders a `TimePickerCard` and blocks until the
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user picks a slot or cancels. The UX is identical to the LangGraph
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version.
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- **interrupt-headless** — Same Strategy B adaptation as
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`gen-ui-interrupt`, but the time-picker popup renders in the app
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surface (outside the chat) instead of inline. Both demos share the
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same backend agent (`InterruptAgentController`).
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- **byoc-hashbrown** — Ported. The hashbrown UI kit
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(`@hashbrownai/react@0.5.0-beta.4`) consumes streaming text and uses
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`useJsonParser` to progressively assemble UI from partial JSON. Spring
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AI's `ChatClient.stream()` streams text tokens, so the hashbrown
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parser tolerates the per-token feed. Final-shape correctness depends on
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the model following the example prompt — there is no guarantee like
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LangGraph's `with_structured_output`.
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- **gen-ui-tool-based** — Ported using `useComponent` per-tool renderers
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bound to `render_bar_chart` / `render_pie_chart` tools. Args stream as
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partial JSON; the Zod schemas accept partials so the chart components
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can render once enough fields are present.
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- **reasoning-custom**, **reasoning-default**,
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**tool-rendering-reasoning-chain** — frontend code is wired for
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`REASONING_MESSAGE_*` events, but the Spring AI handler CANNOT emit
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them. This is a genuine SDK limitation in Spring AI 1.0.1, not an
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adapter or wiring gap. Details below.
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**What the demo needs.** The reasoning UI mounts only when the backend
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emits AG-UI `REASONING_MESSAGE_START` / `_CONTENT` / `_END` events
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(role `"reasoning"`). The canonical `langgraph-python` agent produces
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these by routing the OpenAI model's reasoning summary through the
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**OpenAI Responses API** (`reasoning={"effort": "medium", "summary":
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"detailed"}`). The aimock fixtures for these spring-ai cells
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(`d6/spring-ai/reasoning.json`,
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`d6/spring-ai/tool-rendering-reasoning-chain.json`, copied from
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langgraph-python) carry the reasoning text in a dedicated
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`response.reasoning` field, which aimock renders over the OpenAI
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**chat-completions** wire as streaming `delta.reasoning_content`
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chunks (see `@copilotkit/aimock` `buildTextChunks` —
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`delta: { reasoning_content: slice }`).
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**Why Spring AI 1.0.1 cannot surface it.** The spring-ai integration
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speaks OpenAI chat-completions (`spring-ai-starter-model-openai`,
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`/v1/chat/completions`). In `spring-ai-openai:1.0.1` the streaming
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delta is bound to the record `OpenAiApi.ChatCompletionMessage`, whose
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components are exactly `rawContent, role, name, toolCallId, toolCalls,
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refusal, audioOutput, annotations` — there is **no `reasoning_content`
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/ `reasoning` field**, no metadata map, and no `@JsonAnySetter`
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catch-all. The record is annotated `@JsonIgnoreProperties`, so the
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inbound `reasoning_content` JSON property is **silently discarded at
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deserialization**. It never reaches `ChatResponse` /
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`Generation.getOutput()`, so the Java handler has no API to read it.
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The reasoning-summary channel of the OpenAI **Responses API** is also
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unavailable: `spring-ai-openai:1.0.1` ships no Responses-API client
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(only `OpenAiApi` chat-completions classes exist), so the
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langgraph-python parity path cannot be reproduced either.
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**Why the inline-`<reasoning>`-tag workaround does not apply.** The
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proven `claude-sdk-python` agent PRIMARILY maps Anthropic's native
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extended-thinking channel: it enables `thinking={"type": "enabled", ...}`
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on the Messages API, receives `thinking_delta` blocks, and re-routes
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them to `REASONING_MESSAGE_*`. Only when no native thinking channel is
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present does it FALL BACK to prompting the model to wrap its plan in
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literal `<reasoning>...</reasoning>` text tags inside normal output and
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parsing those tags out of the text stream. The inline-tag fallback IS
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expressible in Spring AI (the handler already streams
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`getOutput().getText()`). But neither claude-sdk path fits these cells:
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the spring-ai aimock fixtures emit reasoning through the dedicated
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`reasoning` field (→ `reasoning_content`), NOT via an Anthropic native
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thinking channel and NOT as inline `<reasoning>` tags in `content`.
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Rewriting the fixtures to embed inline tags — or hand-fabricating a
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reasoning block in the handler — would be a demo-weakening fixture hack
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that misrepresents the integration's real capability, so it is
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deliberately not done.
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**What a real fix requires (upstream / out of scope here).** Either
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(a) Spring AI adds a `reasoning_content` (or reasoning-summary) field
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to its chat-completions delta record and exposes it on
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`Generation`/output metadata; or (b) Spring AI ships an OpenAI
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Responses-API client that surfaces the reasoning summary; or (c) a
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custom `WebClient`-level interceptor parses the raw chat-completions
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SSE for `delta.reasoning_content` BEFORE Spring AI's binding drops it,
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bypassing `ChatClient` entirely (a substantial custom-parser effort
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that re-implements the streaming pipeline). None of these is a
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showcase-side change. Until one lands, these cells ship as frontend
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code (so the pattern is documented end-to-end) and the chat behaves as
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a regular chat with no reasoning block.
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- **multimodal** — the frontend sends image + PDF attachments through
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CopilotChat's `AttachmentsConfig`. Whether the adapter forwards them
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into Spring AI's `UserMessage.media()` surface is
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integration-dependent; the Spring-AI model (`gpt-4.1`) is vision-capable
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on the provider side.
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- **mcp-apps** — the runtime wires the MCP Apps middleware with the public
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Excalidraw MCP server. The middleware injects MCP tools into the AG-UI
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request so the Spring-AI ChatClient sees them, and intercepts tool calls
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to emit activity events. Whether the `ag-ui:spring-ai` adapter forwards
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runtime-injected tools into Spring AI's tool-calling surface is
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integration-dependent; the demo wiring is in place so the cell lights up
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when the adapter supports it.
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## Ported demos
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The full ported list lives in `manifest.yaml`. Highlights include:
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agentic-chat, tool-rendering (default + custom + catchall), frontend-tools
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(+ async), hitl-in-chat (+ booking variant), hitl-in-app, prebuilt-sidebar
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/ popup, chat-slots, chat-customization-css, headless-simple,
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headless-complete, beautiful-chat, auth, readonly-state-agent-context,
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open-gen-ui (+ advanced), voice, agent-config, a2ui-fixed-schema,
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declarative-gen-ui, multimodal, gen-ui-tool-based, mcp-apps,
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byoc-hashbrown, and the three reasoning variants.
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