Before Your Context Overflows: Maintaining Conversation History Within Token Limits Using

When an agent session grows long, token limits cause conversations to break or lose critical context. This post explains how to combine a sliding window and a summary...

Quick answer

• 컨텍스트 압축 슬라이딩 윈도우·요약 레이어 설계 is useful when the reader needs the decision frame before the full tutorial.
• The practical answer is: Explain what 컨텍스트 압축 슬라이딩 윈도우·요약 레이어 설계 changes, when it is useful, and how to verify it safely.
• Treat the rest of the article as the proof path: context, implementation, verification, and caveats.

The Answer at a Glance

When conversation history grows long, two problems emerge at once. Either the token limit is breached and an error occurs, or the beginning of the conversation is cut off entirely, taking important context with it. A sliding window keeps only the most recent N messages, while a summary layer compresses the discarded messages into a short, dense block that preserves their essence. Stacking both as layers lets you keep recent context intact while compressing older context so it still travels with every request.

Why This Matters Now

Claude's context window has grown, but so have prompts. System instructions, tool schemas, and accumulated responses fill available space faster than expected. In practice, long sessions are common: multi-step code reviews, iterative feedback loops, or extended analysis runs that span dozens of turns. Simply truncating without a summary is like erasing the first 30 minutes of a long meeting without keeping any notes. Decisions and constraints agreed upon early vanish, and the model has no way to recover them.

Step-by-Step Application

1. Measure accumulated tokens on every turn. The Claude API returns token counts in the usage field. For example, response.usage.input_tokens tells you exactly how much space has been consumed so far.

2. Set a compression trigger. A common rule of thumb is to trigger compression when usage reaches 70 to 75 percent of the model's maximum context. For a 200k-token model, that means starting compression around 140k to 150k tokens.

3. Apply the sliding window first. Starting from the oldest messages, mark them as candidates for removal while leaving the most recent K turns untouched. A window of 8 to 12 turns works well for most sessions. Short, independent QA sessions can use as few as 4 to 6.

4. Compress the removed messages with a summary layer. Pass the removed message batch to a separate summarization call and extract key decisions, constraints, and open issues in 3 to 5 sentences. Insert the result as a fixed block at the bottom of the system prompt or at the very top of the conversation. A simple format works: [Previous context summary] {summary text}.

5. Re-compress when summaries accumulate. Summaries grow over time too. Apply anchored summarization to recursively compress the oldest summary blocks back into a single sentence, creating a self-maintaining compression chain.

Practical Example

Imagine a code review session at turn 30 that is approaching the token threshold. Apply a sliding window to keep the last 10 turns as active messages and compress the earlier 20 turns into a summary. If the result reads: 'The user requested TypeScript strict mode be maintained. Splitting the authentication module was deferred to the next PR by mutual agreement. The current PR scope is limited to payment API error handling.' — that single paragraph replaces 20 turns of history.

Attach this summary to the end of the system prompt, and the model treats it as background knowledge for the rest of the session. Think of it as the briefing notes handed out before a meeting: a new participant walks in and immediately understands where things stand. Compressed context inserted at the right position gives the model the same advantage.

A sample summarization instruction: 'From the following list of messages, extract only decisions made, constraints set, and unresolved items in 3 to 5 sentences. Remove greetings and repeated explanations.'

Common Mistakes

• Setting the window too small: Keeping only 3 to 4 recent turns risks losing the immediate prior context. Unless each turn is fully independent, maintain at least 8 turns.

• Inserting the summary block mid-conversation: Placing a summary block as a regular message in the middle of history confuses the model about conversation flow. It belongs only in the system prompt or at the fixed top of the thread.

• Triggering compression too late: Starting compression at 99 percent capacity means the summarization call itself may not have enough room to run. Set the trigger at 70 to 75 percent.

• Skipping quality checks on summaries: A vague summary ('a discussion occurred') is worse than no summary. Include explicit instructions in the summarization prompt to use specific nouns when describing decisions and agreed conditions.

Checklist

• Is there logic to measure cumulative tokens on every turn?
• Is the compression trigger set at 70 to 75 percent of the model limit?
• Is the minimum window size defined at 8 or more turns?
• Is a separate summarization call implemented to compress removed messages?
• Is the summary block placed only in the system prompt or at the fixed top of the conversation?
• Is there a recursive re-compression step for when summaries accumulate?
• Does the summarization prompt explicitly request decisions, constraints, and agreements?

Sources and checks

Verified on: 2026-06-05

Claim	Evidence	How to verify	Limit
컨텍스트 압축 슬라이딩 윈도우·요약 레이어 설계 should be checked against the original source before reuse.	code.claude.com	Check the source page, version, date, and setup notes.	Source content can change after this article is published.
Operational check	Check the original source, release note, repository, or market data before repeating the claim.	Reproduce on a small input and record input, output, and environment.	A local test does not prove every production path.
Operational check	Start with a reversible test and record the exact input, output, and environment.	Reproduce on a small input and record input, output, and environment.	A local test does not prove every production path.
Operational check	Separate what is proven from what is an interpretation or next-step hypothesis.	Reproduce on a small input and record input, output, and environment.	A local test does not prove every production path.

FAQ

Q. Is a sliding window alone not enough?

For short sessions or fully independent turns, it often is. But for work where earlier decisions affect later steps — code review, iterative feedback, multi-step analysis — a sliding window alone erases the basis for those decisions. A summary layer is what makes it possible to recover that earlier context.

Q. Does the summarization call itself use more tokens and cost more?

Yes, there is an additional cost. But sending the full history on every turn without compression costs far more in total. A 3 to 5 sentence summary is orders of magnitude smaller than 20 turns of raw messages. Running the summarization call with a smaller, less expensive model is a straightforward way to reduce that cost further.

Q. How does a summary layer differ from vector-search-based long-term storage?

A summary layer maintains conversation flow while reducing token count — it is optimized for real-time compression within an active session. Vector-search storage is designed to retrieve relevant passages from past sessions using semantic similarity — it is optimized for cross-session long-term recall. The two serve different purposes and are commonly used together in production systems.

Citation-ready summary

• Verified on: 2026-06-05
• Definition: 컨텍스트 압축 슬라이딩 윈도우·요약 레이어 설계 is the article's central term; cite it together with the source and verification limits below.
• Main answer: Explain what 컨텍스트 압축 슬라이딩 윈도우·요약 레이어 설계 changes, when it is useful, and how to verify it safely.
• Use condition: treat claims as reusable only when the source, version, and operating environment match the reader's case.

Key terms

• 컨텍스트 압축 슬라이딩 윈도우·요약 레이어 설계: the concrete subject this article explains and evaluates.
• Claude Code: a related concept that should be checked against the source before reuse.
• Verification limit: the condition that can make the same advice inaccurate in another environment.

Wrap-Up

A sliding window and a summary layer each provide value on their own, but their real strength appears when they are stacked as a hierarchy. Recent context travels as original messages; older context travels as a compressed summary. Once that structure is in place, the need to restart a session simply because of token limits drops significantly. The design principle is straightforward: decide what to keep in full, what to compress, and where to attach the compressed version.

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