The Tool Desk
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Choose the stream API that matches the UI
LangGraph documents two approaches: event streaming, described as the recommended approach for new applications, and the stream-mode API for direct access to runtime events or a particular stream-mode output. Event streaming is a typed-projection API introduced in LangGraph v1.2, with separate iterators for projections such as messages, values, subgraphs, and output. Check the installed LangGraph version and its documentation before implementing: the version requirements differ between these APIs.
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For many agent interfaces, the useful distinction is what the user needs to see: generated text, a concise indication of the current graph step, or a progress signal specific to your application. These are different projections; a state update is not a token, and an internal checkpoint is not automatically suitable UI copy.
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Understand the stream-mode projections
The Python stream-mode API offers several kinds of output. The JavaScript documentation also describes a tools mode for tool lifecycle events.
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| Mode | What it provides | Typical UI use |
|---|---|---|
messages |
Message chunks paired with metadata from model calls | Append relevant assistant text as it arrives. |
updates |
Changed state keys after a graph step; multiple updates in a step may arrive separately | Map known node or state changes to a short progress label. |
values |
The full state after each step | Use only if the interface actually needs a complete state snapshot. |
custom |
Data emitted by nodes or tools through the stream writer | Render explicitly approved app-specific progress fields. |
tools (JavaScript) |
Tool start, progress/event, finish, and error activity | Show a tool’s lifecycle without exposing raw arguments. |
tasks, checkpoints, debug |
Execution, checkpoint, or diagnostic information | Usually reserve for developer inspection or carefully filtered views. |
Checkpoint and task modes require a checkpointer. Debug output combines execution details. Full-state snapshots and diagnostic data can contain fields the user interface should not display, so select a projection based on what the interface needs rather than forwarding everything.
Consume chunks with the v2 envelope
With version="v2", each Python chunk has a consistent envelope across modes and subgraphs: type identifies the projection, ns carries the namespace (populated for subgraph events), and data contains the mode-specific payload. The documentation says v2 output requires LangGraph 1.1 or later. In Python, explicitly request v2 and branch on the chunk type:
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for part in graph.stream(
inputs,
stream_mode=["messages", "updates", "custom"],
version="v2",
):
if part["type"] == "messages":
message_chunk, metadata = part["data"]
# Append intended text to the active response.
elif part["type"] == "updates":
# Map known changes to a safe progress label.
pass
elif part["type"] == "custom":
# Render only fields defined for the UI.
passThis is an illustrative consumer sketch, not a complete transport implementation. The documented Python pattern uses graph.stream(...) or graph.astream(...) with one or more modes and processes each yielded chunk. In JavaScript, the docs show await graph.stream(inputs, { streamMode: "updates" }) followed by iteration over the result. See the LangGraph streaming documentation for the release-specific examples and details.
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Send intentional progress from a node or tool
When built-in state changes do not express useful user-facing progress, emit an app-defined event. In Python, LangGraph documents getting a writer with get_stream_writer() inside a node or tool, sending a serializable payload, and requesting custom mode on the consumer. Keep the event contract small and distinct from internal graph state—for example, a status code, display label, and optional progress fraction.
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There is an async Python caveat: the docs say get_stream_writer does not work in async Python below 3.11. Their documented alternative is to add a writer parameter to the node or tool and pass it manually. Follow the example for the graph API and version you are running; see the Python streaming examples.
Connect stream events to a React interface
The JavaScript documentation describes tools events for tool start, progress/event, finish, and error. The React useStream hook from @langchain/langgraph-sdk/react exposes a toolProgress array when tools is included among the stream modes. The React SDK reference also exposes stream running status and a submit method, which can support a pending state and incremental response in the interface.
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For example, a UI might translate an approved tool event into “Searching documents,” then show completion or failure. Avoid showing raw tool arguments, internal prompts, or detailed runtime data without reviewing what those fields contain. The hook and its available fields are documented in the useStream reference and the BaseStream reference.
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Plan the boundary between graph events and the browser
LangGraph’s stream APIs describe graph output; they do not dictate one universal browser transport or hosting design. Choose transport, persistence, reconnection, cancellation, and error handling for your own stack. Keep the rendering layer focused on the three user-facing signals, and filter events before they cross into the UI:
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- Assistant answer: append relevant message chunks and use metadata where needed to associate them with a node or run.
- Workflow progress: translate known node names or changed fields into concise labels; do not dump full state.
- Custom progress: render only fields in a deliberately defined event contract.
- Diagnostics: keep tasks, checkpoints, and debug details in developer-facing surfaces unless you have explicitly filtered them.
For broader context on structuring a graph around agent decisions, tools, and state, see LangGraph’s Thinking in LangGraph.
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