How to intermediate · 3 min read

Handle memory overflow in long conversations

Quick answer

To handle memory overflow in long conversations with AI APIs, use techniques like context window management by truncating or summarizing earlier messages, or store conversation history externally and feed only relevant parts. Implement sliding windows or chunking to keep input size within model limits.

PREREQUISITES

Python 3.8+
OpenAI API key (free tier works)
pip install openai>=1.0

Setup

Install the openai Python package and set your API key as an environment variable.

Install OpenAI SDK: pip install openai
Set environment variable: export OPENAI_API_KEY='your_api_key' (Linux/macOS) or setx OPENAI_API_KEY "your_api_key" (Windows)

bash

pip install openai

output

Collecting openai
  Downloading openai-1.x.x-py3-none-any.whl (xx kB)
Installing collected packages: openai
Successfully installed openai-1.x.x

Step by step

This example demonstrates managing long conversation memory by summarizing earlier messages to keep the context within the model's token limit using gpt-4o.

python

import os
from openai import OpenAI

client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])

# Simulated long conversation history
conversation_history = [
    {"role": "user", "content": "Hello!"},
    {"role": "assistant", "content": "Hi! How can I help you today?"},
    # ... many more messages ...
]

# Function to summarize conversation history

def summarize_history(history):
    summary_prompt = [
        {"role": "system", "content": "Summarize the following conversation briefly."},
        {"role": "user", "content": "".join([msg['content'] for msg in history])}
    ]
    response = client.chat.completions.create(
        model="gpt-4o",
        messages=summary_prompt,
        max_tokens=150
    )
    return response.choices[0].message.content

# Summarize earlier messages to reduce context size
summary = summarize_history(conversation_history[:-2])

# Compose new messages with summary plus recent messages
messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": f"Summary of conversation so far: {summary}"},
] + conversation_history[-2:]

# Send request with trimmed context
response = client.chat.completions.create(
    model="gpt-4o",
    messages=messages
)

print("Assistant reply:", response.choices[0].message.content)

output

Assistant reply: Sure! Based on our previous conversation, how can I assist you further?

Common variations

Other approaches to handle memory overflow include:

Sliding window: Keep only the most recent messages within token limits.
External memory: Store full conversation in a database and retrieve relevant parts dynamically.
Async calls: Use asynchronous SDK calls for better performance in long sessions.
Different models: Use models with larger context windows like gpt-4o or claude-3-5-sonnet-20241022.

python

import os
import asyncio
from openai import OpenAI

async def async_chat():
    client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])
    messages = [
        {"role": "user", "content": "Hello, handle memory overflow in long chats."}
    ]
    response = await client.chat.completions.acreate(
        model="gpt-4o",
        messages=messages
    )
    print("Async reply:", response.choices[0].message.content)

asyncio.run(async_chat())

output

Async reply: To manage memory overflow, summarize or truncate conversation history to fit within token limits.

Troubleshooting

If you encounter context_length_exceeded errors, reduce the number of messages or summarize earlier parts. If responses seem out of context, ensure your summary captures key details. Monitor token usage with SDK tools or logs to stay within limits.

✅

Key Takeaways

Summarize or truncate conversation history to fit model context limits and avoid memory overflow.
Use sliding window or external memory storage to manage long conversations efficiently.
Choose models with larger context windows like gpt-4o for better long chat support.
Implement async calls for improved performance in handling long sessions.
Monitor token usage and handle errors by adjusting input size or summarization.

Verified 2026-04 · gpt-4o, claude-3-5-sonnet-20241022

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