Why Does Genspark Forget Things? Context Window Limits and How to Fix Them

Introduction

"I've been using this chat screen for three days now, so Genspark should understand my project well."

There was a time when I thought that. But the reality was quite the opposite. The longer I used the chat screen, the more "forgetful" Genspark became.

In the previous article, we discussed the problem of Genspark "lying." This time, I'll share my experiences and countermeasures regarding the "AI memory problem," which I deeply felt during the development of a fortune-telling website.

Update: September 2026: Genspark's memory and context-management features have changed considerably since this article was first written in 2025. Today, in addition to AI Drive, Genspark has Hub, which can share files and conversation history across projects, and SecondBrain, a persistent memory layer that can draw from past Genspark projects, files, emails, meetings, and connected apps.

For that reason, the "regularly move to a new chat" workflow described later in this article should be understood as the workaround I used at the time, rather than a rule that every Genspark user needs to follow in 2026.

For a current overview of Genspark, see "What Is Genspark? What You Can Do, Its Benefits, and What I've Learned After 18 Months of Use".

Human Perception vs. AI Reality

Human Expectations

"If we work together for a long time, the other party should learn my way of thinking and background, and their understanding should deepen."

This is true in human relationships, isn't it? When working with the same team members, you can communicate even if you omit explanations.

AI Reality

In reality, it's the opposite:

• Important information gets buried as chat history grows
• Reaching the limit of the context window (memory capacity)
• Unstable access to AI Drive occurred in my long-running chats at the time
• Memory fragmentation progresses, and consistency is lost

The original version of this article included a specific claim that GPT-4 had a 128,000-token context window and used that figure to explain Genspark's behavior.

I have removed that explanation.

Genspark now uses multiple AI models, and the amount of context available depends on the model and the feature being used. In addition, systems such as Hub and SecondBrain can retrieve relevant information from outside the immediate conversation.

That means there is no longer one simple number that can accurately describe "Genspark's memory capacity."

The important point is that a single chat should not be treated as unlimited long-term memory.

I also discuss this more broadly in "AI Memory in Development: What Beginners Get Wrong About Genspark".

Experience 1: On Day 3, the AI Suddenly "Forgot the Specifications"

On the third day of developing the fortune-telling website, we had this conversation.

Me: "Please modify the code according to the Twitter API integration specifications we discussed yesterday."

Genspark: "I apologize. I cannot find any information about the Twitter API integration specifications. Could you please provide more details?"

Huh? But we discussed it extensively yesterday...

The original article stated these causes more definitively, but I could not see Genspark's internal context-processing mechanism. What I actually observed was simpler: information discussed the previous day was no longer being retrieved correctly.

For more detail on the underlying problem, see "3 Strategies to Overcome Genspark's Context Window Limits".

Experience 2: Loss of Access Permissions to AI Drive

Even more serious was the phenomenon of being "unable to read" specification documents stored in Genspark AI Drive.

Me: "Please check /Genspark Development Log/00_AI Instructions.md in AI Drive and tell me the project policy."

Genspark: "I apologize. I cannot access that file."

Even though the file actually existed, the AI could not access it from that chat.

In the original version of this article, I suggested several possible internal causes, including permission tokens not being updated during long chat sessions. However, I had no way to verify Genspark's internal implementation, so I have removed those claims.

What I could actually confirm was:

• The file still existed in AI Drive
• The current chat sometimes failed to access it
• Starting a new chat and loading the file again often restored access

At the time, regularly moving to a new chat screen was therefore a practical workaround.

AI Drive itself still exists in 2026. Genspark's current paid plans include 50 GB of AI Drive storage with Plus and 1 TB with Pro.

However, long-term project context no longer needs to rely on AI Drive alone. Hub and SecondBrain now provide additional ways to preserve and retrieve context, which I'll discuss below.

Context Window Limits

Even the latest AI models have context window limits.

The old version of this article listed the following models: GPT-3.5, GPT-4, GPT-4 Turbo, and Claude 3.5, along with their context-window sizes.

That table has been removed because it is no longer a useful way to explain the current Genspark environment.

Genspark now provides access to multiple AI models, while systems such as Hub and SecondBrain can retrieve relevant information from previous conversations, files, and other sources.

Still, development projects can contain large amounts of information:

• Specification documents
• Long chat histories
• Entire codebases
• Error logs
• Previous design decisions

Even as model context windows become larger, it is still useful to organize important information rather than assuming the AI will always retrieve everything correctly.

Memory Fragmentation: The Most Troublesome Problem

The most troublesome issue in long-term chat is "memory fragmentation."

Symptoms

1. Forgetting previously decided design policies and making contradictory suggestions.
2. Even when told, "We discussed that before," it responds with, "No record found."
3. Forgetting the file structure of AI Drive and stating that existing files "cannot be found."
4. Forgetting the project's background and objectives, making irrelevant suggestions.

The original version of this article stated that this happened because the AI "compresses" and summarizes old information.

That was my interpretation at the time, but I could not verify Genspark's internal mechanism.

What I could observe was that, as the amount of project information increased, older details were sometimes no longer retrieved correctly.

Current features such as Hub and SecondBrain are designed to address exactly this kind of context problem, but they still do not mean that every piece of old information will always be retrieved perfectly.

For important specifications and decisions, I still recommend keeping a document that both you and the AI can refer back to.

Practical Countermeasure: Migrating Chat Screens Every Other Day

The most effective countermeasure I found at the time was to regularly start a new chat screen.

2026 update: I no longer recommend mechanically creating a new chat every one to three days for every project.

Hub now allows related projects to share context, and SecondBrain provides a longer-term memory layer. For ongoing work, using those features is usually more appropriate than treating a new chat as the only solution.

Recommended Patterns I Used at the Time

• Daily migration: Large-scale projects or critical development
• Every other day: Normal development pace
• Within 3 days: Minimum countermeasure

These were my own operational guidelines in 2025, not official Genspark limits.

Chat Migration Steps

1. Create a Work Log in the Current Chat

Today's Work:
- Twitter API integration completed
- Resolved OAuth 1.0a authentication issue
- Next task: Database design

Important Decisions:
- Include media_data parameter in the signature
- Manage environment variables in the .env file

2. Save to AI Drive

"Please save today's work log to /Project Name/Work Logs/2025-12-05.md in AI Drive."

3. Resume in a New Chat Screen

"Please load /Project Name/00_AI Instructions.md and /Work Logs/2025-12-05.md from AI Drive to understand the current project status."

At the time, regular chat screen migration was one of the most effective ways I found to avoid Genspark's memory problems. Today, I would first consider using a Hub for a long-running project and switch chats or projects when the current conversation itself becomes difficult to manage.

For the original chat-migration workflow, see "The Best Time to Transition to a Chat Screen and a 3-Step Guide".

Utilizing AI Drive: Creating an Environment Where Forgetting is Okay

The Genspark AI Drive feature is a powerful solution to this problem.

Key Features of AI Drive

1. Persistent Storage
   • Files remain saved even if the chat screen changes
   • Important project documents can be kept separately from conversation history
2. Structured Information Management
   • Organize files by project
   • Keep specifications, work logs, and reference materials in predictable locations

The basic idea remains the same as when I first wrote this article: don't leave important project knowledge only inside chat history.

Practical Folder Structure

/Project Name/
├── 00_AI Instructions.md          # Overall project outline
├── 01_Design Documents/
│   ├── Project Final Plan.md
│   └── Technical Specifications.md
├── 02_Work Logs/
│   ├── 2025-12-03.md
│   ├── 2025-12-04.md
│   └── 2025-12-05.md
├── 03_Reference Sources/
│   └── Past Project Code/
└── 04_Troubleshooting/
        └── Resolved Issues List.md

Hub: A Better Fit for Long-Running Projects in 2026

One of the biggest differences from when this article was first written is the addition of Genspark Hub.

A Hub is a workspace for grouping related projects together with shared context.

Files uploaded to a Hub are available across conversations in that Hub, and its Cross-Project Context feature allows Genspark to retrieve relevant conversation history from other projects in the same Hub.

For example, you can ask:

"Generate a competitor analysis based on last week's market research project."

or:

"Draft an action plan referencing yesterday's meeting notes project."

Instead of keeping one enormous chat open indefinitely, a more practical setup today may be: one long-running topic or client → one Hub, and then create separate projects inside it for research, development, documentation, meetings, and other tasks.

Hub also supports Custom Instructions that apply across projects, making it useful for rules you do not want to repeat in every conversation.

SecondBrain: Genspark Now Has a Persistent Memory Layer

Genspark took this idea further with SecondBrain, introduced as part of AI Workspace 6.0 in July 2026.

Genspark describes SecondBrain as its memory layer.

It can build context from sources such as:

• Previous Genspark projects
• Files
• Emails
• Meetings
• Chat history
• Calendar data
• Connected apps

Super Agent can then retrieve that context when performing tasks.

This directly addresses one of the biggest frustrations described in the original version of this article: having to repeatedly explain who you are, what the project is, and what has already happened.

That does not make documentation unnecessary.

Instead, I think the situation has changed from 2025: AI forgets, so manually recreate its memory with files, to 2026: Genspark has much better memory systems, but important project knowledge should still be documented and verifiable.

Quick Reference: Supplementing AI's Memory

When specification documents become large, Genspark may not always use every part of the document. This is where "quick references" become effective.

Example of Quick Reference

# Project Quick Reference

## Basic Information
- Project Name: Genspark Development Log
- Objective: Automatic update of Genspark/AI information blog
- Tech Stack: React + TypeScript + Cloudflare

## Important Decisions
1. Use Hatena Blog only (Do not use Twitter)
2. Link to main site once every three posts
3. Post articles on Monday, Wednesday, Friday

## Common Problems and Solutions
1. OAuth authentication error → Include media_data in signature
2. Environment variables not readable → Check .env file placement

By having the AI load this file first, it can quickly grasp the overall picture of the project.

This approach is still useful today.

If you use Hub, you can also place recurring rules, terminology, output formats, and other non-negotiable instructions in Hub's Custom Instructions rather than repeating them manually every time.

Summary: Understanding AI's Limits and Working Effectively With Them

Genspark is a powerful tool, but a single chat does not function like unlimited human long-term memory.

When this article was first written, my main solution was to use AI Drive as "external memory storage." Today, Genspark has added Hub and SecondBrain, making long-term context management much more capable than it was in 2025.

Even so, I still recommend keeping critical specifications, decisions, and work logs in a form that humans can inspect and verify.

Next time, under the theme "Genspark Also Embeds Bugs," we introduce code quality issues generated by AI and debugging methods.