Individual skill
langchain-langgraph-streaming
Pick the correct LangGraph 1.0 stream_mode ("messages" vs "updates" vs "values"), wire it into SSE or WebSocket without proxy-buffering gotchas, and filter astream_events(v2) server-side before forwarding to the browser. Use when building a live-token chat UI, a per-node progress bar, a debug/time-travel view, or diagnosing a LangGraph stream that hangs over a production proxy. Trigger with "langgraph streaming", "stream_mode messages", "stream_mode updates", "stream_mode values", "langgraph SSE", "langgraph astream_events", "SSE hangs behind nginx", "cloud run streaming".
plugins/saas-packs/langchain-py-pack/skills/langchain-langgraph-streaming/SKILL.md
Source commit 0500b275e963c027f6b410e8c49c0446945c574e
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- Last checked
- Nov 12, 2026
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- 2,917
- Repository last updated
- Nov 12, 2026
- License
- MIT
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- ai Repository topic
- automation Repository topic
- claude-code Repository topic
- devops Repository topic
- mcp Repository topic
- ai-agents Repository topic
- anthropic Repository topic
- claude-code-plugins Repository topic
- developer-tools Repository topic
- llm Repository topic
- saas Declared skill tag
- langchain Declared skill tag
- langgraph Declared skill tag
- python Declared skill tag
- langchain-1.0 Declared skill tag
- streaming Declared skill tag
- sse Declared skill tag
- websocket Declared skill tag
- design Derived category
- design-interface Derived category
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SKILL.mdcore · 16,236 bytesSKILL.mdcore · 16,236 bytesSKILL.mdcore · 16,236 bytesSKILL.mdcore · 16,236 bytesSKILL.mdcore · 16,236 bytesSKILL.mdcore · 16,236 bytesSKILL.mdcore · 16,236 bytesSKILL.mdcore · 16,236 bytesSKILL.mdcore · 16,236 bytesSKILL.mdcore · 16,236 bytesSKILL.mdcore · 16,236 bytesreferences/astream-events-filtering.mddoc · 9,497 bytesreferences/one-pager.mddoc · 3,529 bytesreferences/sse-endpoint-template.mddoc · 9,789 bytesreferences/stream-mode-comparison.mddoc · 8,809 bytesreferences/websocket-and-reconnect.mddoc · 10,032 bytes
Read excerpt
# LangGraph Streaming (Python)
## Overview
An engineer ships `stream_mode="values"` to a token-level chat UI because it
"seemed the most complete." Every single token causes the full graph state —
message history, scratchpad, plan — to be re-sent and re-rendered. At ~60
tokens/sec the browser overdraws, the React reconciler can't keep up, the tab
freezes, and users blame the model. The correct answer was `stream_mode="messages"`,
which emits an `AIMessageChunk` delta per token (typically 5-50 bytes) — one
token's worth of DOM work. This is pain-catalog entry **P19** and it is the #1
LangGraph integration mistake in the 1.0 generation.
Then the same UI ships to Cloud Run and hangs forever. No error. No logs. The
server is emitting tokens; they just never reach the browser. Default proxy