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Agents become truly powerful when they can invoke external tools — weather APIs, calculators, web search, database queries, and more. But raw JSON tool results are not what your users want to see. This pattern shows you how to render structured, type-safe UI cards for every tool call your agent makes, complete with loading states and error handling.

How tool calling works

When a LangGraph agent decides it needs external data, it emits one or more tool calls as part of an AI message. Each tool call includes:
  • name — the tool being invoked (e.g. "get_weather", "calculator")
  • args — the structured arguments passed to the tool
  • id — a unique identifier linking the call to its result
The agent runtime executes the tool, and the result comes back as a ToolMessage. The useStream hook unifies all of this into a single toolCalls array you can render directly.

Setting up useStream

The first step is wiring up useStream to your agent backend. The hook returns reactive state including a toolCalls array that updates in real time as the agent streams. Define a TypeScript interface matching your agent’s state schema and pass it as a type parameter to useStream for type-safe access to state values. In the examples below, replace typeof myAgent with your interface name:

The ToolCallWithResult type

Each entry in the toolCalls array is a ToolCallWithResult object:

Filtering tool calls per message

An AI message may trigger multiple tool calls, and your chat may contain many AI messages. To render the right tool cards under each message, filter by matching call.id against the message’s tool_calls array:

Building specialized tool cards

Rather than dumping raw JSON, build dedicated UI components for each tool. Use call.name to select the right card:

Weather card example

Loading and error states

Always handle the pending and error states to give users clear feedback:

Type-safe tool arguments

If your tools are defined with structured schemas, you can use the ToolCallFromTool utility type to get fully typed args:
Using ToolCallFromTool gives you compile-time safety. If the tool schema changes, your UI components will flag type errors immediately.

Rendering tool calls inline with streaming text

Tool calls often arrive interleaved with streamed text. The useStream hook keeps toolCalls in sync with the stream, so pending cards appear as soon as the agent emits the call — before the tool has finished executing. This means users see:
  1. The AI’s text as it streams in
  2. A loading card the moment a tool call is emitted
  3. The card updates to show the result once the tool completes
Tool calls update in place. The same call.id transitions from "pending" to "completed" (or "error"), so your UI simply re-renders the same component with new state.

Handling multiple concurrent tool calls

Agents can invoke several tools in parallel. The toolCalls array will contain multiple entries with state: "pending" simultaneously. Each resolves independently, so your UI should handle partial completion gracefully:

Best practices

Follow these guidelines when building tool call UIs:
  • Always handle all three statespending, completed, and error. Users should never see a blank card.
  • Parse results safely — tool results arrive as strings. Wrap JSON.parse() in a try/catch and show a fallback on parse failure.
  • Provide a generic fallback — not every tool needs a bespoke card. Render a collapsible JSON view for unknown tool names.
  • Show the tool name and args during loading — users want to know what the agent is doing, even before the result arrives.
  • Keep cards compact — tool cards sit inline with chat messages. Avoid overwhelming the conversation with oversized widgets.