> ## Documentation Index > Fetch the complete documentation index at: https://langchain-5e9cc07a-preview-stream-1765228345-2909855.mintlify.site/llms.txt > Use this file to discover all available pages before exploring further. # Streaming LangChain implements a streaming system to surface real-time updates. Streaming is crucial for enhancing the responsiveness of applications built on LLMs. By displaying output progressively, even before a complete response is ready, streaming significantly improves user experience (UX), particularly when dealing with the latency of LLMs. ## Overview LangChain's streaming system lets you surface live feedback from agent runs to your application. What's possible with LangChain streaming: * [**Stream agent progress**](#agent-progress) — get state updates after each agent step. * [**Stream LLM tokens**](#llm-tokens) — stream language model tokens as they're generated. * [**Stream custom updates**](#custom-updates) — emit user-defined signals (e.g., `"Fetched 10/100 records"`). * [**Stream multiple modes**](#stream-multiple-modes) — choose from `updates` (agent progress), `messages` (LLM tokens + metadata), or `custom` (arbitrary user data). ## Supported stream modes Pass one or more of the following stream modes as a list to the [`stream`](https://reference.langchain.com/python/langgraph/graphs/#langgraph.graph.state.CompiledStateGraph.stream) or [`astream`](https://reference.langchain.com/python/langgraph/graphs/#langgraph.graph.state.CompiledStateGraph.astream) methods: | Mode | Description | | ---------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------- | | `updates` | Streams state updates after each agent step. If multiple updates are made in the same step (e.g., multiple nodes are run), those updates are streamed separately. | | `messages` | Streams tuples of `(token, metadata)` from any graph nodes where an LLM is invoked. | | `custom` | Streams custom data from inside your graph nodes using the stream writer. | ## Agent progress To stream agent progress, use the [`stream`](https://reference.langchain.com/python/langgraph/graphs/#langgraph.graph.state.CompiledStateGraph.stream) or [`astream`](https://reference.langchain.com/python/langgraph/graphs/#langgraph.graph.state.CompiledStateGraph.astream) methods with `stream_mode="updates"`. This emits an event after every agent step. For example, if you have an agent that calls a tool once, you should see the following updates: * **LLM node**: [`AIMessage`](https://reference.langchain.com/python/langchain/messages/#langchain.messages.AIMessage) with tool call requests * **Tool node**: [`ToolMessage`](https://reference.langchain.com/python/langchain/messages/#langchain.messages.ToolMessage) with execution result * **LLM node**: Final AI response ```python title="Streaming agent progress" theme={null} from langchain.agents import create_agent def get_weather(city: str) -> str: """Get weather for a given city.""" return f"It's always sunny in {city}!" agent = create_agent( model="gpt-5-nano", tools=[get_weather], ) for chunk in agent.stream( # [!code highlight] {"messages": [{"role": "user", "content": "What is the weather in SF?"}]}, stream_mode="updates", ): for step, data in chunk.items(): print(f"step: {step}") print(f"content: {data['messages'][-1].content_blocks}") ``` ```shell title="Output" theme={null} step: model content: [{'type': 'tool_call', 'name': 'get_weather', 'args': {'city': 'San Francisco'}, 'id': 'call_OW2NYNsNSKhRZpjW0wm2Aszd'}] step: tools content: [{'type': 'text', 'text': "It's always sunny in San Francisco!"}] step: model content: [{'type': 'text', 'text': 'It's always sunny in San Francisco!'}] ``` ## LLM tokens To stream tokens as they are produced by the LLM, use `stream_mode="messages"`. Below you can see the output of the agent streaming tool calls and the final response. ```python title="Streaming LLM tokens" theme={null} from langchain.agents import create_agent def get_weather(city: str) -> str: """Get weather for a given city.""" return f"It's always sunny in {city}!" agent = create_agent( model="gpt-5-nano", tools=[get_weather], ) for token, metadata in agent.stream( # [!code highlight] {"messages": [{"role": "user", "content": "What is the weather in SF?"}]}, stream_mode="messages", ): print(f"node: {metadata['langgraph_node']}") print(f"content: {token.content_blocks}") print("\n") ``` ```shell title="Output" expandable theme={null} node: model content: [{'type': 'tool_call_chunk', 'id': 'call_vbCyBcP8VuneUzyYlSBZZsVa', 'name': 'get_weather', 'args': '', 'index': 0}] node: model content: [{'type': 'tool_call_chunk', 'id': None, 'name': None, 'args': '{"', 'index': 0}] node: model content: [{'type': 'tool_call_chunk', 'id': None, 'name': None, 'args': 'city', 'index': 0}] node: model content: [{'type': 'tool_call_chunk', 'id': None, 'name': None, 'args': '":"', 'index': 0}] node: model content: [{'type': 'tool_call_chunk', 'id': None, 'name': None, 'args': 'San', 'index': 0}] node: model content: [{'type': 'tool_call_chunk', 'id': None, 'name': None, 'args': ' Francisco', 'index': 0}] node: model content: [{'type': 'tool_call_chunk', 'id': None, 'name': None, 'args': '"}', 'index': 0}] node: model content: [] node: tools content: [{'type': 'text', 'text': "It's always sunny in San Francisco!"}] node: model content: [] node: model content: [{'type': 'text', 'text': 'Here'}] node: model content: [{'type': 'text', 'text': ''s'}] node: model content: [{'type': 'text', 'text': ' what'}] node: model content: [{'type': 'text', 'text': ' I'}] node: model content: [{'type': 'text', 'text': ' got'}] node: model content: [{'type': 'text', 'text': ':'}] node: model content: [{'type': 'text', 'text': ' "'}] node: model content: [{'type': 'text', 'text': "It's"}] node: model content: [{'type': 'text', 'text': ' always'}] node: model content: [{'type': 'text', 'text': ' sunny'}] node: model content: [{'type': 'text', 'text': ' in'}] node: model content: [{'type': 'text', 'text': ' San'}] node: model content: [{'type': 'text', 'text': ' Francisco'}] node: model content: [{'type': 'text', 'text': '!"\n\n'}] ``` ## Custom updates To stream updates from tools as they are executed, you can use [`get_stream_writer`](https://reference.langchain.com/python/langgraph/config/#langgraph.config.get_stream_writer). ```python title="Streaming custom updates" theme={null} from langchain.agents import create_agent from langgraph.config import get_stream_writer # [!code highlight] def get_weather(city: str) -> str: """Get weather for a given city.""" writer = get_stream_writer() # [!code highlight] # stream any arbitrary data writer(f"Looking up data for city: {city}") writer(f"Acquired data for city: {city}") return f"It's always sunny in {city}!" agent = create_agent( model="claude-sonnet-4-5-20250929", tools=[get_weather], ) for chunk in agent.stream( {"messages": [{"role": "user", "content": "What is the weather in SF?"}]}, stream_mode="custom" # [!code highlight] ): print(chunk) ``` ```shell title="Output" theme={null} Looking up data for city: San Francisco Acquired data for city: San Francisco ``` If you add [`get_stream_writer`](https://reference.langchain.com/python/langgraph/config/#langgraph.config.get_stream_writer) inside your tool, you won't be able to invoke the tool outside of a LangGraph execution context. ## Stream multiple modes You can specify multiple streaming modes by passing stream mode as a list: `stream_mode=["updates", "custom"]`. The streamed outputs will be tuples of `(mode, chunk)` where `mode` is the name of the stream mode and `chunk` is the data streamed by that mode. ```python title="Streaming multiple modes" theme={null} from langchain.agents import create_agent from langgraph.config import get_stream_writer def get_weather(city: str) -> str: """Get weather for a given city.""" writer = get_stream_writer() writer(f"Looking up data for city: {city}") writer(f"Acquired data for city: {city}") return f"It's always sunny in {city}!" agent = create_agent( model="gpt-5-nano", tools=[get_weather], ) for stream_mode, chunk in agent.stream( # [!code highlight] {"messages": [{"role": "user", "content": "What is the weather in SF?"}]}, stream_mode=["updates", "custom"] ): print(f"stream_mode: {stream_mode}") print(f"content: {chunk}") print("\n") ``` ```shell title="Output" theme={null} stream_mode: updates content: {'model': {'messages': [AIMessage(content='', response_metadata={'token_usage': {'completion_tokens': 280, 'prompt_tokens': 132, 'total_tokens': 412, 'completion_tokens_details': {'accepted_prediction_tokens': 0, 'audio_tokens': 0, 'reasoning_tokens': 256, 'rejected_prediction_tokens': 0}, 'prompt_tokens_details': {'audio_tokens': 0, 'cached_tokens': 0}}, 'model_provider': 'openai', 'model_name': 'gpt-5-nano-2025-08-07', 'system_fingerprint': None, 'id': 'chatcmpl-C9tlgBzGEbedGYxZ0rTCz5F7OXpL7', 'service_tier': 'default', 'finish_reason': 'tool_calls', 'logprobs': None}, id='lc_run--480c07cb-e405-4411-aa7f-0520fddeed66-0', tool_calls=[{'name': 'get_weather', 'args': {'city': 'San Francisco'}, 'id': 'call_KTNQIftMrl9vgNwEfAJMVu7r', 'type': 'tool_call'}], usage_metadata={'input_tokens': 132, 'output_tokens': 280, 'total_tokens': 412, 'input_token_details': {'audio': 0, 'cache_read': 0}, 'output_token_details': {'audio': 0, 'reasoning': 256}})]}} stream_mode: custom content: Looking up data for city: San Francisco stream_mode: custom content: Acquired data for city: San Francisco stream_mode: updates content: {'tools': {'messages': [ToolMessage(content="It's always sunny in San Francisco!", name='get_weather', tool_call_id='call_KTNQIftMrl9vgNwEfAJMVu7r')]}} stream_mode: updates content: {'model': {'messages': [AIMessage(content='San Francisco weather: It's always sunny in San Francisco!\n\n', response_metadata={'token_usage': {'completion_tokens': 764, 'prompt_tokens': 168, 'total_tokens': 932, 'completion_tokens_details': {'accepted_prediction_tokens': 0, 'audio_tokens': 0, 'reasoning_tokens': 704, 'rejected_prediction_tokens': 0}, 'prompt_tokens_details': {'audio_tokens': 0, 'cached_tokens': 0}}, 'model_provider': 'openai', 'model_name': 'gpt-5-nano-2025-08-07', 'system_fingerprint': None, 'id': 'chatcmpl-C9tljDFVki1e1haCyikBptAuXuHYG', 'service_tier': 'default', 'finish_reason': 'stop', 'logprobs': None}, id='lc_run--acbc740a-18fe-4a14-8619-da92a0d0ee90-0', usage_metadata={'input_tokens': 168, 'output_tokens': 764, 'total_tokens': 932, 'input_token_details': {'audio': 0, 'cache_read': 0}, 'output_token_details': {'audio': 0, 'reasoning': 704}})]}} ``` ## Disable streaming In some applications you might need to disable streaming of individual tokens for a given model. This is useful when: * Working with [multi-agent](/oss/python/langchain/multi-agent) systems to control which agents stream their output * Mixing models that support streaming with those that do not * Deploying to [LangSmith](/langsmith/home) and wanting to prevent certain model outputs from being streamed to the client Set `streaming=False` when initializing the model. ```python theme={null} from langchain_openai import ChatOpenAI model = ChatOpenAI( model="gpt-4o", streaming=False # [!code highlight] ) ``` When deploying to LangSmith, set `streaming=False` on any models whose output you don't want streamed to the client. This is configured in your graph code before deployment. Not all chat model integrations support the `streaming` parameter. If your model doesn't support it, use `disable_streaming=True` instead. This parameter is available on all chat models via the base class. See the [LangGraph streaming guide](/oss/python/langgraph/streaming#disable-streaming-for-specific-chat-models) for more details. ## Related * [Streaming with chat models](/oss/python/langchain/models#stream) — Stream tokens directly from a chat model without using an agent or graph * [Streaming with human-in-the-loop](/oss/python/langchain/human-in-the-loop#streaming-with-hil) — Stream agent progress while handling interrupts for human review * [LangGraph streaming](/oss/python/langgraph/streaming) — Advanced streaming options including `values`, `debug` modes, and subgraph streaming *** [Edit the source of this page on GitHub.](https://github.com/langchain-ai/docs/edit/main/src/oss/langchain/streaming.mdx) [Connect these docs programmatically](/use-these-docs) to Claude, VSCode, and more via MCP for real-time answers.