Fix context window full error fast

The context window full error happens when a chat exceeds the model's token budget or when a UI bug blocks memory handling. First, identify if it is a true token limit or a platform bug. Then recover by trimming old messages and inserting a concise summary so the conversation can continue without losing important context.

Why the context window full error happens

There are two common causes. First, the model hit its token budget: the stored messages, plus the new prompt and the response, exceed the model's maximum context length. In API calls you can see this in an error or a 400 response that mentions the model's maximum context length. Second, a UI or server bug can surface the message chat memory full error even for new chats, where the platform's cached memory store is inconsistent. Check forum reports like the one about new chats showing the error to see if it is an outage or platform bug.

How to tell a UI bug from a true token limit

API behaviour: a true token limit usually returns an API error that references the model or maximum context length. If you control the API call, inspect the HTTP status and body.
New chats failing: if brand new conversations show context window full error, it is likely a UI/server bug. See reports from other developers about new chats failing for confirmation.
Token estimate: estimate tokens locally. If your aggregated message text length is much smaller than the model context window, suspect the UI. A simple heuristic is tokens approx text length / 4.
Reproducibility: reproduce the error via API and UI. If only the web UI shows it, open a support ticket and check status forums.

Step-by-step fix to recover right now (ol)

Export the conversation. Save the full message list to a file or database so nothing is lost.
Estimate token usage. Use a simple estimator in your automation: tokens = Math.ceil(text.length / 4). In an n8n Code node you can compute this for each message.
Prune oldest messages until estimated tokens are below your safe threshold, for example 75 percent of model limit. Move pruned messages to an archive table or file.
Summarize the pruned chunk into one compact system message. Send a summarization prompt to the model that returns a short, factual summary, then insert that single summary message into the conversation context.
Retry the user request. The conversation now contains the recent messages plus the summary, keeping intent while staying under the token limit.
If the error persists across fresh chats, check platform status and OpenAI forum threads for a possible bug, and temporarily disable memory features until resolved.

Automated memory pruning and summarization flow you can deploy

Below is a deployable pattern you can implement in n8n, Make.com or Zapier. The goal is automatic detection, pruning, summarizing and reinserting a compact context message so you never hit the limit in production.

Core components

Persistent store for messages, for example a Postgres table, Airtable base or Google Sheet. Keep fields: conversation_id, role, content, created_at.
Trigger: incoming message webhook. In n8n use the Webhook node. In Make.com use a scenario triggered by your incoming HTTP or app module. In Zapier use a Zap with a webhook or app trigger.
Token estimator: in n8n the Code node runs a small JavaScript function to sum token estimates. In Zapier use a Code by Zapier step. In Make.com use a JavaScript module.
Summarizer: call the OpenAI Chat Completions API via the HTTP Request node in n8n, the HTTP module in Make.com, or Webhooks by Zapier. Ask the model to return a short summary and key facts only.

Example n8n snippet for token estimate and prune (place in a Code node)

// Input: items contain messages with item.json.content
const MAX_TOKENS = 32000; // set to model limit or safe threshold
let total = 0;
let keep = [];
for (const item of items) {
  const text = item.json.content || '';
  const tokens = Math.ceil(text.length / 4);
  total += tokens;
  keep.push({tokens, text, json: item.json});
}
// prune oldest until total under threshold, e.g. 0.75 * MAX_TOKENS
const threshold = Math.floor(MAX_TOKENS * 0.75);
while (total > threshold && keep.length) {
  const removed = keep.shift();
  total -= removed.tokens;
  // send removed.json to archive table via DB node
}
return keep.map(k => ({json: k.json}));

Summarization prompt example to send in the HTTP Request node body

Summarize the following messages into a short system message with key facts and dates, max 120 words. Keep intent and user preferences. Messages: [paste concatenated pruned texts]

Tool specific notes: n8n, Make.com and Zapier

n8n: use the Webhook node to receive chat input, store messages in a database via the Postgres node or Google Sheets node, use the Code node to estimate tokens and the HTTP Request node to call OpenAI. Use the Set node to assemble the new message array and then the workflow that calls the model.
Make.com: build a scenario with an incoming trigger module, use an Airtable or Google Sheets module for storage, a JavaScript module to estimate tokens, and the HTTP module to call the OpenAI Chat Completions API. Modules provide clear logging for debugging token decisions.
Zapier: use a Zap to collect messages and store them in Google Sheets or Airtable, use a Code by Zapier step to estimate tokens and prune, then use Webhooks by Zapier to call OpenAI. Insert the returned summary as a new row or field.

How to stop the problem happening again

Always keep a rolling summary message for each conversation so you rarely store full histories. Replace long tails with a single system summary.
Monitor estimated token totals in production. Emit alerts when a conversation hits 60 percent of your model limit, not at the limit.
Archive old conversations automatically after inactivity, and purge or compress their messages monthly.
Provide a manual override in your UI to force summarize now, and surface the exact error text to help triage when support is needed.

When to get help

If the error appears on fresh chats or across many unrelated accounts, check community reports and OpenAI status. Platform bugs have been discussed by developers in threads such as the one about context window full - UNREASONABLE LIMIT and the thread on context window full error appearing in completely NEW chats. If you confirm an API error with details you cannot resolve, collect request IDs, timestamps and the minimal reproducer and open a support ticket with OpenAI or the platform provider.

If your bot frequently reaches the limit despite pruning, consider a lighter memory strategy: only store user preferences and objectives in the long form and rely on short recent messages for dialogue flow. For related problems where responses stop mid reply, see our post Fix ChatGPT stops mid response.

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Frequently asked questions

What exactly does "context window full error" mean?

It means the model cannot fit the stored messages, new prompt and expected response inside its maximum context length. That limit is in tokens. Sometimes the platform surfaces this as a UI error even if the API would have succeeded, so check API responses and message sizes.

How do I quickly recover a conversation that hit the error?

Export the conversation, prune the oldest messages until you are under a safe token threshold, create a short summary of the pruned messages and insert that summary as a single context message. Retry the user request after reinserting the summary.

Can I automate pruning so I never hit the limit?

Yes. Build a flow that estimates tokens (simple heuristic tokens = text.length / 4), prunes old messages, archives them, calls the model to summarize the archive, then inserts one summary message. Implementable in n8n with Webhook, Code node and HTTP Request node.

When is it a bug instead of hitting a token limit?

If new chats or many accounts show the error simultaneously, or if your estimated tokens are far below model limits, it is likely a UI or server bug. Check developer forums for reports and gather logs and request IDs before contacting support.

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