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packages/@n8n/nodes-langchain/utils/agent-execution/processEventStream.ts

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+import type { StreamEvent } from '@langchain/core/dist/tracers/event_stream';
+import type { IterableReadableStream } from '@langchain/core/dist/utils/stream';
+import type { AIMessageChunk, MessageContentText } from '@langchain/core/messages';
+import type { IExecuteFunctions } from 'n8n-workflow';
+
+import type { AgentResult, ToolCallRequest } from './types';
+
+/**
+ * Processes the event stream from a streaming agent execution.
+ * Handles streaming chunks, tool calls, and intermediate steps.
+ *
+ * This is a generalized version that can be used across different agent types
+ * (Tools Agent, OpenAI Functions Agent, etc.).
+ *
+ * @param ctx - The execution context
+ * @param eventStream - The stream of events from the agent
+ * @param itemIndex - The current item index
+ * @returns AgentResult containing output and optional tool calls/steps
+ */
+export async function processEventStream(
+	ctx: IExecuteFunctions,
+	eventStream: IterableReadableStream<StreamEvent>,
+	itemIndex: number,
+): Promise<AgentResult> {
+	const agentResult: AgentResult = {
+		output: '',
+	};
+
+	const toolCalls: ToolCallRequest[] = [];
+
+	ctx.sendChunk('begin', itemIndex);
+	for await (const event of eventStream) {
+		// Stream chat model tokens as they come in
+		switch (event.event) {
+			case 'on_chat_model_stream':
+				const chunk = event.data?.chunk as AIMessageChunk;
+				if (chunk?.content) {
+					const chunkContent = chunk.content;
+					let chunkText = '';
+					if (Array.isArray(chunkContent)) {
+						for (const message of chunkContent) {
+							if (message?.type === 'text') {
+								chunkText += (message as MessageContentText)?.text;
+							}
+						}
+					} else if (typeof chunkContent === 'string') {
+						chunkText = chunkContent;
+					}
+					ctx.sendChunk('item', itemIndex, chunkText);
+
+					agentResult.output += chunkText;
+				}
+				break;
+			case 'on_chat_model_end':
+				// Capture full LLM response with tool calls for intermediate steps
+				if (event.data) {
+					const chatModelData = event.data;
+					const output = chatModelData.output;
+
+					// Check if this LLM response contains tool calls
+					if (output?.tool_calls && output.tool_calls.length > 0) {
+						// Collect tool calls for request building
+						// Note: For Gemini, we pass additional_kwargs to ALL tool calls
+						// so the signature can be applied to each when rebuilding
+						for (const toolCall of output.tool_calls) {
+							toolCalls.push({
+								tool: toolCall.name,
+								toolInput: toolCall.args,
+								toolCallId: toolCall.id || 'unknown',
+								type: toolCall.type || 'tool_call',
+								log:
+									output.content ||
+									`Calling ${toolCall.name} with input: ${JSON.stringify(toolCall.args)}`,
+								messageLog: [output],
+								// Pass additional_kwargs to ALL tool calls so signature is available
+								additionalKwargs: output.additional_kwargs as Record<string, unknown> | undefined,
+							});
+						}
+					}
+				}
+				break;
+			default:
+				break;
+		}
+	}
+	ctx.sendChunk('end', itemIndex);
+
+	// Include collected tool calls in the result
+	if (toolCalls.length > 0) {
+		agentResult.toolCalls = toolCalls;
+	}
+
+	return agentResult;
+}