This model's maximum context length is 8192 tokens however you requested

Not specified by the original author.

This model's maximum context length is 8192 tokens, however you requested 9175 tokens

TL;DR

Your request is bigger than the model's context window. When this hits during document embedding, the fix is to split oversized texts into token-bounded chunks before sending them, and to retry only the failed batches. Trimming the input is not optional; the request will never succeed as-is.

The error

This model's maximum context length is 8192 tokens, however you requested 9175 tokens (9175 in the messages, 0 in the completion). Please reduce the length of the messages or completion.

Fix it

1. Count tokens before you send. Use tiktoken with the model's encoding and measure every text. Expected: you know exactly which texts exceed the limit.
2. Split oversized texts into chunks that fit, leaving headroom for the completion. A common pattern is chunks of a few hundred tokens with small overlaps. Expected: no chunk exceeds the model's limit.
3. Re-run the failed batch only. Keep a record of which document IDs failed and re-embed just those. Expected: the retry succeeds because every request now fits.
4. For chat calls, trim history or use a model with a bigger window. Expected: the prompt fits and the call returns 200.
5. If single documents legitimately exceed the window, chunk them at ingestion time and store chunk-level embeddings. Expected: search still works, on smaller pieces.

When to use this

• An agent sees the context-length 400 error on Azure OpenAI during embedding or chat.
• A search-ingestion pipeline dies on one huge document.

When NOT to use this

• 429 rate-limit errors. Those are quota, not size; retrying blindly is right there and wrong here.
• "Unsupported parameter" errors. Those are API shape problems, not size problems.

Compatibility

• Azure OpenAI Service, all embedding and chat models. Token limits vary by model; the fix pattern is the same.

Variant phrasings

• "contextlengthexceeded"
• "This model's maximum context length is 128000 tokens"
• "Please reduce the length of the messages"

Root cause

The limit is per request, not per document. One oversized text in a batch fails the whole batch, which is why ingestion pipelines see this as flaky: most batches pass, then one document kills a batch. The Azure-Samples fix for this exact error was to chunk oversized texts into token-bounded pieces before embedding.

Edge cases

• Token counts differ between the tokenizer you use locally and the model's. Measure with the model's own encoding.
• Overlap between chunks helps retrieval quality but costs tokens. Keep overlap small.

Azure-Samples/azure-search-openai-demo PR #3025 (merged, fixes issue #2971): bulk document embedding against Azure OpenAI failed with "This model's maximum context length is 8192 tokens, however you requested 9175 tokens" whenever a single oversized document landed in a batch. The merged fix splits oversized texts into token-bounded chunks before sending them to the embedding model and retries failed batches at the chunk level. This is the canonical real-world fix for the error in an ingestion pipeline: the request can never succeed at its original size, so chunking is required, not optional.

Not specified by the original author.

TL;DR: The input is bigger than the model's context window. This error is not retryable; retrying the same oversized input fails the same way every time. Check for accidental duplication: the same big document appended every turn is the usual culprit in agent loops. Log message token counts per turn until you find the growth.

The error

langchain_core.exceptions.ContextOverflowError

The fix

1. Summarize the conversation when it gets long. In create_agent, add SummarizationMiddleware with a token trigger so history condenses automatically.
2. Trim messages explicitly with trim_messages from langchain.messages / langchain-core before the call.
3. Check for accidental duplication: the same big document appended every turn is the usual culprit in agent loops. Log message token counts per turn until you find the growth.
4. Switch to a model with a larger window if the input is legitimately large.

When to use this

• You are seeing this exact error message; match the block above, not just part of it.
• The failing call matches the scenario in the title: ContextOverflowError.
• You want the fastest verified fix before digging through logs.

When not to use this

• Your error text differs from the block above; close cousins often have different causes.
• The stack trace points at a different component than the one in the title.
• You already applied this fix and the error persists; look for a second cause instead of reapplying.

Compatibility

• Not pinned to a specific version; follows current ContextOverflowError behavior.

Error catalog entry grounded in the official model exceptions docs and v1 middleware (summarization), verified 2026-09-26. Models: https://docs.langchain.com/oss/python/langchain/models ; middleware: https://docs.langchain.com/oss/python/langchain/middleware/built-in

Not specified by the original author.

TL;DR

Use a tokenizer for the model family to measure messages plus the requested max output against the model's context window.

Steps

1. The error: HTTP 400, type invalidrequesterror, code contextlengthexceeded. Typical message: "This model's maximum context length is N tokens. However, your messages resulted in M tokens."
2. What the agent should do:
3. Classify it as terminal. Retrying the same request is guaranteed to fail; do not wrap it in backoff.
4. Count tokens before calling. Use a tokenizer for the model family to measure messages plus the requested max output against the model's context window.
5. Pick the fix by cost: trim or summarize conversation history (cheapest), reduce max output tokens, or switch to a model with a larger window (for example GPT-4.1 class models carry 1M).
6. For long conversations, build a sliding window or summarization step so growth is bounded, instead of failing at the worst moment mid-session.
7. The trap: confusing a length finishreason on a 200 response (output truncated, request was fine) with this 400 (request rejected). Also do not mistake per-field limits for context overflow; see the stringabovemaxlength skill.
8. Evidence: https://platform.openai.com/docs/guides/error-codes

When to use

You are seeing this: The error: HTTP 400, type invalidrequesterror, code contextlengthexceeded. Use this skill when you run into "OpenAI 400 contextlengthexceeded: this error is terminal, trim input never retry".

When not to use

If your error message or symptom does not match what is described above, this is probably not your fix. Search for your exact error text instead of forcing this one to fit.

Versions

No specific versions are mentioned in the source material, so treat the fix as generally applicable and check the examples against whatever you have installed.

Why this happens

The original report does not dig into a root cause. It documents the symptom and the fix that resolved it.

Edge cases

• Also do not mistake per-field limits for context overflow; see the stringabovemax_length skill.

Sources

• https://platform.openai.com/docs/guides/error-codes

Verified against official OpenAI docs 2026-09-26.