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Retain the smallest amount of history that is sufficient for the agent’s next decision. Keep the current request, the system and developer rules, active goals and constraints, unresolved commitments, immediate tool state, and the recent turns needed to interpret references such as “that file.” Summarize older material into a structured state, and move durable facts and documents to a separately retrievable store. There is no reliable rule such as “keep the last 20 messages”: token counts, task risk, model behavior, cost, latency, and privacy requirements matter more than turn count.
Why a fixed number of turns fails
A conversation turn is not a useful unit of memory. One user message may contain two sentences; another may contain a long contract, a stack trace, or a tool response with thousands of tokens. Keeping ten turns can therefore produce either too little context or an expensive, noisy prompt.
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The right policy is measured against outcomes. Your agent should be able to recover relevant earlier references, obey exact constraints, correct a previous decision, and continue after a tool call. At the same time, irrelevant history should not distract it, inflate input-token charges, increase latency, or expose information that the user expected to be deleted.
| Question | What to measure |
|---|---|
| Task recall | Can the agent find an earlier requirement, definition, or decision when it is needed? |
| Reasoning precision | Does unrelated history cause distraction, contradiction, or context rot? |
| Cost and latency | How many input tokens and retrieval operations are sent on each turn? |
| Reliability | Did summarization preserve negations, numbers, provenance, and unfinished work? |
| Governance | Can a user inspect, correct, or delete stored memory, and are sensitive details excluded? |
| Operational behavior | What happens when the budget is exceeded: compaction, retrieval, truncation, or a new session? |
Context windows are budgets, not targets
A model’s context window covers the tokens supplied as input and the tokens it generates; for some models it also includes reasoning tokens. OpenAI’s documentation uses gpt-4o-2024-08-06 as an example with a 128,000-token total context window. That figure describes one model and date, not a recommended amount of conversation to retain.
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A larger limit does not make a full transcript automatically better. Anthropic describes “context rot” this way: “as the number of tokens in the context window increases, the model’s ability to accurately recall information from that context decreases.” Redundant tool output, stale instructions, and several similar versions of a document compete for attention even when they fit technically.
Provider limits also change. A 2025 OpenAI session-memory cookbook cites GPT-5 configurations of up to 272,000 input tokens and 128,000 output tokens. Treat those as model-specific documentation examples. Before setting a production limit, check the deployed model’s current input, output, and reasoning allowances and how your SDK counts them.
A four-layer retention architecture
Separating active context from durable memory prevents the prompt from becoming a database. Each layer has a different purpose and deletion policy.
1. Active raw window
Keep the latest turns that can affect the next action. This normally includes the current user message, the immediately preceding exchange, relevant system and developer instructions, recent tool calls and results, and the message that introduced a still-active reference such as “use the second attachment.” Remove or summarize a turn once it no longer changes what the agent should do.
2. Structured running summary
When older turns leave the active window, replace them with a compact state object or clearly labeled summary. Preserve the user’s goal, decisions, constraints, definitions, named entities, assumptions, unresolved questions, commitments, and next actions. Keep exact values, dates, commands, and quotations when wording matters. Add timestamps or message identifiers when an auditor must trace a statement back to its origin.
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3. Retrievable long-term store
Persist durable preferences, project facts, approved decisions, documents, and tool artifacts outside the prompt. Store them with provenance, timestamps, access controls, and a deletion mechanism. At each turn, retrieve only passages relevant to the current task rather than injecting the entire archive. Retrieval can come from files, a database, or the web; the model can use only what your application supplies in the conversation.
4. Budget and trigger logic
Count tokens before every model call. Reserve capacity for the new user message, system instructions, tool calls, reasoning, and the expected answer. Trigger compaction before the hard limit, leaving a safety margin for an unusually large tool result. A useful policy is to compact when the estimated prompt plus reserved completion allowance crosses a threshold, not when the provider rejects the request.
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How to choose a retention budget
Start with the smallest budget that passes your task tests, then increase it only when a measurable failure requires more context. Define separate allowances for the raw window, the running summary, retrieved passages, and the model’s response. For example, an implementation might reserve the newest turns for conversational continuity, cap the summary at a fixed token count, and allow retrieval to contribute only a few ranked passages. The exact numbers depend on your model, tool payloads, and answer length; they should be configuration values rather than assumptions hidden in code.
Do not spend the entire provider window on input. If a model advertises a total limit, subtract the maximum output you need and a margin for reasoning or serialization overhead. A support agent that may return a 2,000-token explanation needs a different input ceiling from a classification agent whose response is one JSON object.
Turn count can still be a secondary guardrail. Use it to prevent an unexpectedly long sequence of tiny messages from bypassing token checks, but make token usage and task state the primary triggers.
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When to summarize or compact history
Compaction is the act of summarizing a conversation that is approaching its limit and starting the next context with that summary. Trigger it before a request fails, after a major phase of work, or when the active transcript contains repeated tool output that is no longer needed verbatim.
- Measure: count the tokens in system instructions, active messages, tool results, retrieved text, and the reserved output budget.
- Identify dependencies: mark references, open tasks, decisions, exact values, and tool state that the next step still needs.
- Write a structured summary: separate facts from assumptions, completed work from pending work, and user requirements from agent suggestions.
- Verify loss-sensitive items: check negations, numbers, names, dates, permissions, error messages, and quoted wording against the raw transcript.
- Start a fresh context: include the summary, the current request, applicable instructions, and only the tool results needed for the next action.
- Keep an escape hatch: retain a pointer to the original messages so an authorized retrieval step can recover exact wording.
OpenAI’s session-memory guidance warns that even a large window can be overwhelmed by uncurated histories, redundant tool results, or noisy retrievals. Its recommended safeguards include summarization and token-pressure checks.
What belongs in a summary—and what does not
| Keep in the running summary | Usually omit or retrieve on demand |
|---|---|
| Current objective and success criteria | Greetings, acknowledgements, and conversational filler |
| Explicit user constraints, exclusions, and permissions | Repeated copies of the same instruction |
| Decisions already accepted and their rationale | Superseded drafts and abandoned alternatives |
| Unresolved questions, blockers, and next actions | Large tool payloads whose relevant fields are already extracted |
| Definitions, names, identifiers, dates, and exact values that affect execution | Documents that can be fetched from an authoritative store |
| Provenance and confidence for consequential facts | Secrets or sensitive attributes not required for the task |
Label uncertainty explicitly. “User has not approved the migration” is materially different from “migration approved.” A summary that drops a negation can produce a confident but unauthorized action.
Session history is not long-term memory
An SDK session can retain the transcript for continuity while your application maintains a separate, consolidated memory. OpenAI’s Agents SDK documentation describes controlling how much session history is retrieved without changing what remains stored in the session. That distinction lets you keep an audit trail while presenting the model with a focused working set.
Long-term entries should be atomic and editable: one preference, decision, or project fact per record when practical. Attach an owner, creation time, last-confirmed time, source message, and sensitivity classification. When a user corrects a fact, update or supersede the old record instead of allowing both versions to be retrieved as if they were current.
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Retrieval should be relevance- and permission-filtered before ranking. A semantically similar passage is not necessarily authorized, current, or applicable to this project. Return provenance with each passage so the agent can distinguish a user-approved decision from an unverified note.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Choosing among retention policies
| Policy | Strength | Failure mode | Best fit |
|---|---|---|---|
| Full transcript | Preserves exact wording and chronology | High cost, latency, distraction, and privacy exposure | Short, high-stakes sessions where the transcript is small |
| Recency window | Simple and inexpensive | Forgets older constraints and decisions | Stateless or low-risk chats with little cross-turn dependency |
| Summary plus recent turns | Balances continuity and token use | Can lose a number, negation, or provenance during compression | Most multi-step assistants |
| Summary plus retrieval | Scales to long projects and document-heavy work | Retrieval can miss, mis-rank, or expose the wrong record | Coding, research, support, and workflow agents with durable state |
| New session after compaction | Clear context boundary and predictable budget | Requires a trustworthy handoff summary | Long-running jobs and agents with distinct phases |
OpenAI’s memory guidance notes that an overlong conversation may be truncated to fit a context window, preserving the beginning and end. Do not rely on that provider behavior as your memory design: important middle turns can disappear without a task-aware summary.
Designing tool-aware memory
Tool calls often consume more tokens than user messages. Store the durable result, not every intermediate payload. For a database query, keep the query, timestamp, authorization context, and rows or fields that informed the decision. For a file operation, keep the path, version or checksum when available, and the outcome. For a failed call, preserve the error and the attempted parameters if the agent may need to retry.
Before invoking another tool, expose only the state that tool needs. After it returns, extract the facts that affect the plan and place bulky raw output in the retrievable store. This keeps the next prompt small while preserving an audit trail.
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Testing whether your policy works
Build regression conversations that intentionally exercise memory boundaries. Include references to an old turn, a correction to a previous decision, a long tool response, a phase transition, and a user deletion request. Run each scenario with a full transcript, a recency window, a summary, and a summary-plus-retrieval policy.
- Score whether the agent recalls the right constraint and refuses a superseded one.
- Check exact numbers, dates, identifiers, and quoted text against the source.
- Measure input tokens, retrieval calls, latency, and failed requests.
- Inject irrelevant history and look for changes in the answer.
- Delete or correct a stored memory and verify that later retrieval no longer returns it.
- Test tool failure and resume behavior after compaction or a new session.
Track contradiction rate after summarization separately from ordinary answer accuracy. A summary can preserve the topic while silently reversing a permission or deadline, which is a more serious defect than a minor wording error.
Privacy, deletion, and governance
Only retain information that has a purpose for the agent’s future work. Keep secrets out of summaries unless the next authorized action genuinely requires them, and prefer references to credentials held by a secure tool. Define retention periods for raw transcripts, summaries, and durable memories independently; a user who deletes a memory should not have to delete an entire account to remove one fact.
Give operators and users a way to inspect what will be retrieved, correct it, and request deletion. Record provenance and access decisions for sensitive projects. If a task crosses users, workspaces, or tenants, apply those boundaries before retrieval—not after the model has seen the text.
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- Identify the deployed model’s current input, output, and reasoning limits.
- Reserve output and tool-result capacity before setting an input ceiling.
- Keep current instructions, active goals, unresolved work, references, and immediate tool state in the raw window.
- Compact before the hard limit with a structured, loss-checked summary.
- Store durable facts and documents separately with provenance, timestamps, permissions, and deletion controls.
- Retrieve narrowly ranked passages instead of replaying an entire archive.
- Use token pressure, task phase, and dependency changes as triggers; use turn count only as a secondary guardrail.
- Run regression tests for recall, contradictions, retrieval errors, cost, latency, and privacy.
The resulting policy is adaptive: preserve exact history while it is cheap and relevant, compress it when the next action no longer depends on every word, and retrieve older material only when the task calls for it.
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