wehub-resource-sync
106 lines
5.2 KiB
Markdown
106 lines
5.2 KiB
Markdown
# Parity Notes
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Baseline: `showcase/integrations/langgraph-python/`.
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This document lists demos present in the langgraph-python reference that
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are **not** ported to the PydanticAI showcase, with the reason for each
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skip. See `manifest.yaml` for the full list of demos that **are** shipped
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in this package.
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## Recently ported demos
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- `gen-ui-tool-based` — Tool-based generative UI with frontend-registered
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`render_bar_chart` and `render_pie_chart` components. The PydanticAI
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agent uses a chart-viz system prompt and relies on the runtime to
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surface frontend-registered tool definitions on each run; no backend
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tool implementations are required because the components handle
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rendering directly on the client.
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## New demos (post-PR #4271)
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The following demos introduced on main via PR #4271 are now ported:
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- `byoc-json-render` — `@json-render/react` BYOC pattern with
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JSONUIProvider wrapping `<Renderer />` and MetricCard children
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forwarding (both post-#4271 fixes preserved).
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- `byoc-hashbrown` — `@hashbrownai/react` BYOC pattern. The PydanticAI
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agent emits the post-#4271 JSON envelope
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`{"ui": [{componentName: {"props": {...}}}]}` verbatim.
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- `multimodal` — image / PDF attachments. Images flow to GPT-4o vision
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natively via `OpenAIResponsesModel`. PDFs are flattened to inline
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text via `pypdf` inside a PydanticAI `history_processors` hook
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(equivalent to langgraph-python's `_PdfFlattenMiddleware`). The
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frontend's `onRunInitialized` shim and LFS-pointer guard are kept
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intact.
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- `voice` — audio transcription. Route reuses the main sales agent at
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the PydanticAI root as a neutral backing agent; the
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`GuardedOpenAITranscriptionService` + direct-instance wiring pattern
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is lifted verbatim from the langgraph-python reference.
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- `agent-config` — `forwardedProps` routing. The TS runtime route
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subclasses `HttpAgent` to repack provider `properties` into an AG-UI
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`context` entry tagged `agent-config-properties`; the Python agent's
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dynamic `@agent.system_prompt` reads that entry. Framework-specific
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adaptation vs. langgraph-python's `forwardedProps.config.configurable`
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path — user-visible behaviour is identical.
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- `auth` — bearer token auth. Gate is built on
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`createCopilotRuntimeHandler` from `@copilotkit/runtime/v2` with the
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`onRequest` hook, framework-agnostic. Post-#4271 fixes preserved
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(default authenticated, `ChatErrorBoundary`, inverted button labels).
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## Skipped demos
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- `mcp-apps` — Requires CopilotKit MCP Apps middleware wired to a remote
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MCP server (Excalidraw). The PydanticAI integration exposes tools via
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`agent.to_ag_ui()` and does not yet have a documented MCP-apps path
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through the Python SDK's A2UI middleware as of this writing. Can be
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revisited when `copilotkit-sdk-python` grows first-class MCP client
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support across AG-UI integrations. The `beautiful-chat` and
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`headless-complete` cells are ported with their Excalidraw suggestion
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pills intentionally omitted (the rest of each cell is at parity).
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- `agentic-chat-reasoning`, `reasoning-default-render`,
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`tool-rendering-reasoning-chain` — These three demos depend on
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`deepagents.create_deep_agent` to emit reasoning/thinking tokens
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alongside regular tool calls. PydanticAI has its own reasoning model
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support (`OpenAIResponsesModel` with reasoning enabled) but does not
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currently stream reasoning content as AG-UI `THINKING_*` events
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through `agent.to_ag_ui()`. Skipped until that bridge exists; a faked
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version would not reflect the real integration.
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- `gen-ui-interrupt`, `interrupt-headless`, `hitl-in-chat`,
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`hitl-in-chat-booking` — All four demos are built on LangGraph's
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`interrupt()` primitive (pauses graph execution mid-tool-call and
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surfaces the payload to the client via `useInterrupt`). PydanticAI
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does not have an equivalent interrupt/resume primitive — its tools
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run to completion. Skipped as framework-specific. (The HITL
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experience in this package is delivered via the `hitl` and
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`hitl-in-app` cells, which use a frontend-tool-driven approval
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pattern that does not require backend interrupts.)
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## Parked for future parity
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These demos are intentionally not in scope for the PydanticAI package
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today — none remain now that the post-#4271 demos listed above have
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landed.
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## Partial parity — ported with documented gaps
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The following demos are in the package but do not ship 100% of the
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langgraph-python behaviour:
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- `headless-complete` — ported without the Excalidraw-via-MCP
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suggestion (see `mcp-apps` skip above). The reasoning-message branch
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of `use-rendered-messages.tsx` is omitted because `@ag-ui/core@0.0.43`
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(the version pinned in this package) does not export
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`ReasoningMessage` and PydanticAI's AG-UI adapter does not emit
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reasoning content today. All other rendering surfaces (per-tool
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renderers, frontend components, default catch-all) are at parity.
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- `beautiful-chat` — ported without the Excalidraw-via-MCP suggestion.
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Shared todo state uses PydanticAI's `StateSnapshotEvent` (emitted on
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`manage_todos` completion) instead of langgraph-python's
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`StateStreamingMiddleware` per-token deltas — the shared todo list
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still syncs, just without per-character streaming animation. All
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other surfaces (A2UI fixed + dynamic, Open Generative UI, HITL,
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frontend tools) are at parity.
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