Imported from jeremylongshore/tons-of-skills-marketplace (plugins/saas-packs/langchain-py-pack/skills/langchain-langgraph-streaming/SKILL.md). Install upstream with npx skills add jeremylongshore/tons-of-skills-marketplace --skill langchain-langgraph-streaming. Copyright stays with the author (MIT).

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 buffering (Nginx, Cloud Run's HTTP/1.1 path, Cloudflare Free) holds the last chunk waiting for more bytes. This is P46 — SSE streams from LangGraph drop the final end event over proxies that buffer — and the fix is three headers: X-Accel-Buffering: no, Cache-Control: no-cache, Connection: keep-alive.

And then the debug view starts crashing browser tabs on long runs. The engineer forwarded astream_events(version="v2") raw to the client because "it has more detail" — but v2 emits thousands of events per invocation (per-token, per-node, per-runnable lifecycle), and a 60-second agent run easily hits 3,000 events. Browsers freeze on the JSON deserialize queue. This is P47 — filter server-side, forward only on_chat_model_stream tokens (and optionally on_tool_start / on_tool_end).

This skill ships the decision matrix, a production-grade FastAPI SSE endpoint with the anti-buffering headers and a 15-second heartbeat, a server-side v2 event filter that drops ~90% of noise, and a WebSocket variant with reconnect-by-thread_id that resumes from the LangGraph checkpointer. Pin: langgraph 1.0.x, langchain-core 1.0.x. Pain-catalog anchors: P19, P46, P47, P48, P67, plus P16 for the thread_id rule and P22 for checkpointer persistence.

Prerequisites

• Python 3.10+
• langgraph >= 1.0, < 2.0, langchain-core >= 1.0, < 2.0
• fastapi >= 0.110, uvicorn[standard] (for SSE/WebSocket hosting)
• A checkpointer: langgraph.checkpoint.memory.MemorySaver for dev, or langgraph.checkpoint.postgres.PostgresSaver for prod
• Access to deploy behind your actual proxy (Nginx / Cloud Run / Cloudflare) — localhost does not reproduce the buffering class of bugs

Instructions

Step 1 — Pick the right stream_mode for your UI

The three modes emit fundamentally different payloads. Match the mode to the UI shape before writing any server code.

Decision tree:

Do you need LLM tokens rendered live in the UI?
├── Yes → stream_mode="messages"
│         (add "updates" to the list if you also want per-node progress)
└── No, I need per-step progress
    ├── Full state for debug/replay? → stream_mode="values"
    └── Just what changed (most UIs)  → stream_mode="updates"

Full payload samples and combined-mode examples are in Stream Mode Comparison.

Step 2 — Wire a minimal SSE endpoint

import asyncio, json
from fastapi import FastAPI
from fastapi.responses import StreamingResponse
from langchain_core.messages import HumanMessage
from langgraph.checkpoint.memory import MemorySaver
from app.graph import build_graph

app = FastAPI()
graph = build_graph(checkpointer=MemorySaver())


def sse(event: str, data: dict) -> str:
    return f"event: {event}\ndata: {json.dumps(data, default=str)}\n\n"


async def stream_tokens(thread_id: str, user_input: str):
    config = {"configurable": {"thread_id": thread_id}}
    async for chunk, metadata in graph.astream(
        {"messages": [HumanMessage(user_input)]},
        config=config,
        stream_mode="messages",
    ):
        # chunk.content may be list[dict] on Claude tool-use turns (P02)
        text = chunk.text if hasattr(chunk, "text") else (
            chunk.content if isinstance(chunk.content, str) else None
        )
        if text:
            yield sse("token", {"text": text, "node": metadata.get("langgraph_node")})
    yield sse("done", {"thread_id": thread_id})

Always use graph.astream(...) (async). Never call graph.stream(...) (sync) from inside an async handler — it blocks the event loop and one slow request blocks every other connection (P48).

Step 3 — Set the anti-buffering headers

@app.get("/stream")
async def stream(thread_id: str, q: str):
    return StreamingResponse(
        stream_tokens(thread_id, q),
        media_type="text/event-stream",
        headers={
            "X-Accel-Buffering": "no",    # Nginx / Cloud Run / Cloudflare
            "Cache-Control": "no-cache",  # Block intermediate caches
            "Connection": "keep-alive",   # Hold the TCP connection
        },
    )

These three headers are non-negotiable in production. Without them, your stream works on localhost and hangs on Cloud Run. See SSE Endpoint Template for the full template with a 15-second heartbeat (required to survive Cloud Run's 60s idle timeout and corporate-proxy timeouts) plus reverse-proxy snippets for Nginx, Traefik, and Cloud Run.

Step 4 — Filter astream_events(version="v2") server-side

If your UI needs richer events than "messages" provides — tool start/end, progress markers, retrieval events — do not forward astream_events raw. A single 60-second agent run can emit 3,000+ events. Filter on the server and forward only what the browser uses.

FORWARD = {"on_chat_model_stream", "on_tool_start", "on_tool_end"}

async def filtered(graph, inputs, config):
    async for event in graph.astream_events(inputs, config=config, version="v2"):
        kind = event["event"]
        if kind == "on_chat_model_stream":
            chunk = event["data"]["chunk"]
            text = chunk.text if hasattr(chunk, "text") else None
            if text:
                yield {"type": "token", "text": text,
                       "node": event["metadata"].get("langgraph_node")}
        elif kind == "on_tool_start":
            yield {"type": "tool_start", "tool": event["name"]}
        elif kind == "on_tool_end":
            yield {"type": "tool_end", "tool": event["name"]}
        # Drop: on_chain_*, on_parser_*, on_prompt_*, on_retriever_* (P47)

Never use astream_log() in new code — soft-deprecated in 1.0 (P67), scheduled for removal in 2.0. Use astream_events(version="v2") instead. Full event taxonomy and compression/backpressure patterns in Astream Events Filtering.

Step 5 — WebSocket variant with reconnect

Use WebSocket instead of SSE when the user may cancel, interrupt, or send follow-up messages mid-stream. WebSocket also sidesteps Cloudflare Free's default response buffering.

from fastapi import WebSocket, WebSocketDisconnect

@app.websocket("/ws/{thread_id}")
async def ws(websocket: WebSocket, thread_id: str):
    await websocket.accept()
    config = {"configurable": {"thread_id": thread_id}}  # P16 — always
    try:
        while True:
            msg = json.loads(await websocket.receive_text())
            if msg["type"] == "user_message":
                async for chunk, metadata in graph.astream(
                    {"messages": [HumanMessage(msg["text"])]},
                    config=config,
                    stream_mode="messages",
                ):
                    text = chunk.text if hasattr(chunk, "text") else None
                    if text:
                        await websocket.send_json({"type": "token", "text": text})
                await websocket.send_json({"type": "done"})
    except WebSocketDisconnect:
        pass  # Checkpointer persists state; reconnect with same thread_id resumes

Because LangGraph checkpointers persist state per thread_id, a client that reconnects to /ws/{same-thread-id} automatically sees the prior conversation history on the next turn — no special "resume" handshake required for between-turn reconnects. For mid-stream reconnects and cancellation handling, see WebSocket & Reconnect.

Step 6 — Run the proxy-readiness checklist before shipping

A stream that works on uvicorn --reload main:app on your laptop will hang behind Cloud Run. Before you ship, walk this checklist:

• Deployed endpoint returns Content-Type: text/event-stream (or 101 Switching Protocols for WebSocket)
• Deployed endpoint returns X-Accel-Buffering: no and Cache-Control: no-cache
• curl -N https://your.app/stream?... shows tokens arriving incrementally — NOT all at once at the end
• Cloud Run deployed with --use-http2 (HTTP/2 end-to-end flushes chunks reliably)
• If behind Cloudflare: Free plan may buffer — test on Pro or use a paid page rule to disable response buffering (or switch to WebSocket)
• 15-second heartbeat configured (: heartbeat\n\n SSE comment every 15s) so idle streams don't get killed by the 60s timeout
• Long-idle load test: run a tool-using agent that waits 45s on an API call; stream should stay alive

Test behind your actual proxy, not just localhost.

Output

• stream_mode chosen deliberately from the decision matrix ("messages" for tokens, "updates" for progress, "values" for debug)
• Minimal FastAPI SSE endpoint using graph.astream(..., stream_mode="messages") in an async handler
• Required anti-buffering headers (X-Accel-Buffering, Cache-Control, Connection) on the StreamingResponse
• Server-side astream_events(version="v2") filter that forwards only on_chat_model_stream + on_tool_start + on_tool_end
• Optional WebSocket variant with thread_id required at the route and checkpointer-backed resume
• Proxy-readiness checklist walked end-to-end before shipping past localhost

Error Handling

Examples

Live-token chat UI (the default case)

stream_mode="messages" plus SSE plus the three anti-buffering headers. One token per SSE frame (~5-50 bytes each), 30-80 frames/sec during active model generation, heartbeat every 15s during tool waits. See SSE Endpoint Template for the complete FastAPI example including heartbeat, reverse-proxy config, and the client EventSource code.

Per-node progress bar for a multi-step agent

stream_mode="updates" yields one event per node (typically 2-20 per invocation). Render as discrete status ticks: "Planning..." → "Searching..." → "Summarizing..." → "Done." Payload is tiny (~100 bytes per tick). Combine with "messages" (stream_mode=["updates", "messages"]) to show both progress ticks and streaming tokens in the active node's pane. Full payload samples in Stream Mode Comparison.

Debug / time-travel view with "values"

stream_mode="values" yields the entire graph state after each node. Useful for state replay, test recording, observability pipelines — not for browser UIs where state size × steps × re-render quickly freezes the tab. Pipe to a server-side log (or LangSmith), not to the browser. Example and caveats in Stream Mode Comparison.

WebSocket with reconnect-by-thread_id

When users can cancel mid-stream or send follow-up messages before the previous turn finishes. The thread_id is required at the route; the checkpointer persists history; reconnecting with the same thread_id automatically sees prior turns. Cancellation is implemented via asyncio.Task.cancel() on the active astream iteration. Worked example with half-open connection detection in WebSocket & Reconnect.

Resources

• LangGraph streaming how-to
• LangGraph streaming concepts
• LangChain astream_events v2
• FastAPI StreamingResponse
• FastAPI WebSockets
• Cloud Run HTTP/2 end-to-end
• Nginx proxy_buffering directive
• Pack pain catalog: docs/pain-catalog.md (entries P16, P19, P22, P46, P47, P48, P67)