docs: add comprehensive Vercel AI SDK integration analysis #595
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Add detailed analysis of how pgflow client can integrate with Vercel AI SDK's useChat hook and related features. Document includes: - Architecture analysis of both systems - Three integration patterns (backend API, custom transport, hybrid) - Event mapping between pgflow and AI SDK stream protocol - Type safety considerations and implementation examples - Advanced use cases (multi-step progress, tool calling, multimodal) - Production recommendations and testing strategies - Comparison with alternative approaches This enables pgflow to be used as a backend for chat applications built with Vercel AI SDK while leveraging pgflow's workflow orchestration and database-backed state management.
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Provides critical analysis of when the pgflow + AI SDK integration makes sense versus when to use AI SDK alone. Key findings: - Strong fit (20% of users): Production enterprise apps with complex multi-step workflows, state persistence needs, observability requirements - Poor fit (80% of users): Simple chatbots, prototypes, speed-critical apps - Trade-offs documented: complexity overhead, latency, vs. reliability gains Includes decision framework, competitive comparison (LangChain, Temporal), and gradual adoption path recommendation. Honest assessment prevents over-engineering while highlighting genuine value for complex use cases.
Implements a frontend-first approach where pgflow client runs in the browser as a custom ChatTransport, eliminating the need for backend API routes. Key components: - PgflowChatTransport: Custom transport implementing ChatTransport interface, connects useChat to pgflow via Supabase Realtime - StreamingContext: API for pgflow steps to emit incremental data (text chunks, reasoning, custom data) broadcast via Realtime - Helper utilities: streamOpenAIResponse, streamAISDKResponse for common streaming patterns - Example flow: Multi-step chat with intent classification, context retrieval, and streaming LLM responses - Example React component: Full chat UI with progress indicators Architecture benefits: - Direct browser → Supabase connection (no API routes) - Real-time streaming via WebSocket (Supabase Realtime) - Type-safe end-to-end (Flow types → UI types) - RLS policies enforce security - Automatic reconnection on network failures This approach is cleaner than backend API routes and leverages pgflow's event-driven architecture naturally. All streaming happens via Supabase Realtime channels, making implementation straightforward. Includes comprehensive documentation in FRONTEND_TRANSPORT_DESIGN.md with implementation roadmap, comparison tables, and production considerations.
Provides executive summary of the frontend-first integration approach: - Why frontend transport is better than backend API routes - Key components (StreamingContext, PgflowChatTransport) - Complete flow examples (backend + frontend) - Implementation requirements for pgflow core - Architecture diagrams and security model - Performance characteristics and migration guide - Comparison with alternatives This document serves as the quick-start guide for developers who want to understand the integration without reading the full technical design.
Comprehensive analysis of frontend transport viability with focus on edge runtime timeout recovery - the critical failure scenario identified. Key additions: 1. EDGE_CASES_AND_RECOVERY.md (12k words): - Detailed analysis of edge runtime shutdown mid-stream - Ephemeral vs durable streaming event storage - Four recovery solutions with trade-offs - Latency analysis: frontend transport 3-5x slower (91-231ms vs 28-48ms) - Viability assessment per use case - Decision tree for architecture choice 2. Streaming context with persistence: - Dual-write pattern: Realtime (fast) + Database (durable) - Batch writes to reduce overhead (10 chunks or 1s interval) - Checkpoint support for long-running operations - Automatic finalization with cleanup - Migration SQL for streaming_chunks table 3. PgflowChatTransport with recovery: - Timeout detection (default 30s) - Chunk replay from database on reconnection - Graceful partial response handling - Automatic recovery attempts - Comprehensive error handling 4. Complete production example: - End-to-end implementation with all scenarios - Testing guide for 4 recovery scenarios - Performance monitoring code - Cost analysis ($9/month for 1000 daily chats) - Production checklist Critical findings: - ✅ Viable for multi-step pipelines (Perplexity-style) - ✅ Recoverable with chunk storage -⚠️ 3-5x slower per token than direct SSE -⚠️ Requires careful timeout management - ❌ Not suitable for low-latency chat Recommendation: Use frontend transport for complex multi-step workflows where intermediate progress visibility matters more than token-level latency. Implement chunk storage for production reliability.
Based on critical user feedback identifying two key issues:
1. Supabase Realtime is NOT streaming - it's pub/sub messaging with
discrete events, not continuous data flow. Each token = separate
network round-trip with JSON overhead. This explains 3-5x latency.
2. Edge runtime timeout affects ALL edge functions, not just pgflow.
Any proxy that streams LLM responses has this issue (25s limit on
Vercel Edge). This is a universal problem, not architecture-specific.
The right solution:
- Use Node.js runtime API routes (300s timeout, not 25s)
- Stream LLM tokens via traditional SSE (28-48ms latency)
- Use Realtime ONLY for coarse-grained events (step completions)
- Let pgflow handle multi-step orchestration in database
- Map pgflow events to SSE data chunks for progress updates
Architecture:
Frontend useChat → Node.js API route → SSE streaming (tokens)
↕
Realtime (step events)
↕
Pgflow (orchestration)
Benefits:
- Fast token streaming (no Realtime overhead)
- Long timeouts (300s+, no edge issues)
- Simple architecture (standard patterns)
- Low cost (~/month vs /month)
- Pgflow benefits preserved (orchestration, observability)
This is the sensible approach. Frontend transport via Realtime should
only be used if you truly need browser→database direct connection
(offline-first, browser extensions, no backend allowed).
For 99% of use cases, this hybrid approach is better.
Based on user's insight: separate preparation from streaming.
Two-phase approach:
1. Pgflow flow: Multi-step preparation (search, rank, extract)
- Durable, stored in database
- Can take 30s+, no timeout issues
- Frontend shows progress via Realtime events
2. Separate streaming endpoint: Simple LLM proxy
- Reads preparation output from DB
- Streams LLM response via standard SSE
- Fast (28-48ms per token)
- Can use Edge runtime (25s is fine for proxying)
Flow:
User message → POST /api/prepare (starts pgflow)
↓ (wait for completion, show progress)
Context ready → POST /api/stream (proxy with context)
↓ (standard SSE streaming)
Response displayed
Advantages:
- Clean separation: pgflow = orchestration, stream = proxy
- Fast streaming (no pgflow overhead)
- Reusable context (can regenerate without re-preparing)
- Simple streaming endpoint (easy to test)
- Can cache preparation results
- Flexible frontend (polling or Realtime)
This is the best architecture for Perplexity-style multi-step apps:
- Complex preparation (search/rank/extract)
- Simple streaming (just proxy with context)
- Show step-by-step progress
- May regenerate response multiple times
Much cleaner than mixing pgflow events with SSE in same endpoint.
The best solution based on user's vision: wire pgflow step events
directly to AI SDK chunks in a single unified stream.
Key concept: ONE continuous SSE stream that includes:
1. Pgflow step events → AI SDK data chunks (preparation progress)
2. LLM tokens → AI SDK text-delta chunks (final response)
User experience:
User: 'What is quantum computing?'
AI: [Searching knowledge base...]
[Found 15 results]
[Ranking by relevance...]
[Extracting key information...]
[Generating response...]
Quantum computing is a type of...
All in one useChat conversation!
Implementation:
- Single /api/chat endpoint (Node.js, 300s timeout)
- Starts pgflow flow
- Subscribes to step events via Realtime
- Converts pgflow events → SSE data chunks
- step:started → data-progress (status started)
- step:completed → data-{stepName} (result) + data-progress (completed)
- When preparation completes, starts LLM streaming
- Streams LLM tokens → text-delta chunks
- Frontend uses standard useChat with onData handler
Event mapping:
Pgflow: { event_type: 'step:completed', step_slug: 'search', output: {...} }
→ SSE: data: {"type":"data-search","data":{...}}
→ useChat onData receives chunk
→ Display progress in UI
Advantages:
- Everything in one conversation (unified UX)
- Real-time progress updates (pgflow events)
- Fast LLM streaming (standard SSE, 28-48ms)
- Standard patterns (useChat, no custom transport)
- Pgflow benefits preserved (orchestration, durability)
Perfect for Perplexity-style apps where users want to see:
- What the AI is doing (searching, ranking, analyzing)
- Intermediate results (found X results, selected top Y)
- Final response with sources
This is the architecture to implement for multi-step AI chat with
real-time progress visibility.
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Add detailed analysis of how pgflow client can integrate with Vercel AI
SDK's useChat hook and related features. Document includes:
This enables pgflow to be used as a backend for chat applications built
with Vercel AI SDK while leveraging pgflow's workflow orchestration and
database-backed state management.