How do you implement streaming in LangChain?

Streaming in LangChain means receiving model output token-by-token rather than waiting for the full response. This dramatically improves perceived responsiveness in user-facing applications. LCEL chains support streaming out of the box through three methods: stream(), astream(), and astream_events().

Synchronous streaming — iterates over chunks as they arrive:

from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser

chain = ChatPromptTemplate.from_template("{topic}") | ChatOpenAI() | StrOutputParser()

for chunk in chain.stream({"topic": "Explain quantum entanglement briefly"}):
    print(chunk, end="", flush=True)

Async streaming — for FastAPI / async servers:

async for chunk in chain.astream({"topic": "..."}):
    print(chunk, end="", flush=True)

Fine-grained event streaming — astream_events() gives you granular events for every component in the chain (tool calls, retriever results, LLM tokens), letting you build rich streaming UIs that show intermediate steps:

async for event in chain.astream_events({"topic": "..."}, version="v2"):
    if event["event"] == "on_chat_model_stream":
        print(event["data"]["chunk"].content, end="")

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