langchain-rate-limits

LangChain Rate Limits

Overview

Handle API rate limits gracefully with built-in retries, exponential backoff, concurrency control, provider fallbacks, and custom rate limiters.

Provider Rate Limits (2026)

Provider	Model	RPM	TPM
OpenAI	gpt-4o	10,000	800,000
OpenAI	gpt-4o-mini	10,000	4,000,000
Anthropic	claude-sonnet	4,000	400,000
Anthropic	claude-haiku	4,000	400,000
Google	gemini-1.5-pro	360	4,000,000

RPM = requests/minute, TPM = tokens/minute. Actual limits depend on your tier.

Strategy 1: Built-in Retry (Simplest)

import { ChatOpenAI } from "@langchain/openai";

// Built-in exponential backoff on 429/500/503
const model = new ChatOpenAI({
  model: "gpt-4o-mini",
  maxRetries: 5,      // retries with exponential backoff
  timeout: 30000,     // 30s timeout per request
});

// This automatically retries on rate limit errors
const response = await model.invoke("Hello");

Strategy 2: Concurrency-Controlled Batch

import { ChatOpenAI } from "@langchain/openai";
import { ChatPromptTemplate } from "@langchain/core/prompts";
import { StringOutputParser } from "@langchain/core/output_parsers";

const chain = ChatPromptTemplate.fromTemplate("Summarize: {text}")
  .pipe(new ChatOpenAI({ model: "gpt-4o-mini", maxRetries: 3 }))
  .pipe(new StringOutputParser());

const inputs = articles.map((text) => ({ text }));

// batch() with maxConcurrency prevents flooding the API
const results = await chain.batch(inputs, {
  maxConcurrency: 5,  // max 5 parallel requests
});

Strategy 3: Provider Fallback on Rate Limit

import { ChatOpenAI } from "@langchain/openai";
import { ChatAnthropic } from "@langchain/anthropic";

const primary = new ChatOpenAI({
  model: "gpt-4o-mini",
  maxRetries: 2,
  timeout: 10000,
});

const fallback = new ChatAnthropic({
  model: "claude-sonnet-4-20250514",
  maxRetries: 2,
});

// Automatically switches to Anthropic if OpenAI rate-limits
const resilientModel = primary.withFallbacks({
  fallbacks: [fallback],
});

const chain = prompt.pipe(resilientModel).pipe(new StringOutputParser());

Strategy 4: Custom Rate Limiter

class TokenBucketLimiter {
  private tokens: number;
  private lastRefill: number;

  constructor(
    private maxTokens: number,    // bucket size
    private refillRate: number,   // tokens per second
  ) {
    this.tokens = maxTokens;
    this.lastRefill = Date.now();
  }

  async acquire(): Promise<void> {
    this.refill();
    while (this.tokens < 1) {
      const waitMs = (1 / this.refillRate) * 1000;
      await new Promise((r) => setTimeout(r, waitMs));
      this.refill();
    }
    this.tokens -= 1;
  }

  private refill() {
    const now = Date.now();
    const elapsed = (now - this.lastRefill) / 1000;
    this.tokens = Math.min(this.maxTokens, this.tokens + elapsed * this.refillRate);
    this.lastRefill = now;
  }
}

// Usage: 100 requests per minute
const limiter = new TokenBucketLimiter(100, 100 / 60);

async function rateLimitedInvoke(chain: any, input: any) {
  await limiter.acquire();
  return chain.invoke(input);
}

Strategy 5: Async Batch with Semaphore

async function batchWithSemaphore<T>(
  chain: { invoke: (input: any) => Promise<T> },
  inputs: any[],
  maxConcurrent = 5,
): Promise<T[]> {
  let active = 0;
  const results: T[] = [];
  const queue = [...inputs.entries()];

  return new Promise((resolve, reject) => {
    function next() {
      while (active < maxConcurrent && queue.length > 0) {
        const [index, input] = queue.shift()!;
        active++;
        chain.invoke(input)
          .then((result) => {
            results[index] = result;
            active--;
            if (queue.length === 0 && active === 0) resolve(results);
            else next();
          })
          .catch(reject);
      }
    }
    next();
  });
}

// Process 100 items, 5 at a time
const results = await batchWithSemaphore(chain, inputs, 5);

Python Equivalent

from langchain_openai import ChatOpenAI
from langchain_anthropic import ChatAnthropic
from langchain_core.runnables import RunnableConfig

# Built-in retry
llm = ChatOpenAI(model="gpt-4o-mini", max_retries=5, request_timeout=30)

# Fallback
primary = ChatOpenAI(model="gpt-4o-mini", max_retries=2)
fallback = ChatAnthropic(model="claude-sonnet-4-20250514")
robust = primary.with_fallbacks([fallback])

# Batch with concurrency control
results = chain.batch(
    [{"text": t} for t in texts],
    config=RunnableConfig(max_concurrency=10),
)

Error Handling

Error	Cause	Fix
429 Too Many Requests	Rate limit hit	Increase maxRetries, reduce maxConcurrency
Timeout	Response too slow	Increase timeout, check network
QuotaExceeded	Monthly limit hit	Upgrade tier or switch provider
Batch partially fails	Some items rate limited	Use .batch() with returnExceptions: true

Resources

• OpenAI Rate Limits
• Anthropic Rate Limits
• LangChain Batch Processing

Next Steps

Proceed to langchain-security-basics for security best practices.