In a working AI agent, retain means selecting information and saving it beyond the current model call; recall means finding relevant saved information and bringing it into a later step’s context. Saving a transcript is persistence, but it is not necessarily useful memory: an agent still needs a way to identify what may matter, retrieve it at the right time, and correct it when it becomes stale.
Retain and recall are separate operations
Retention is the write side of memory. An application may save every message and tool call, or it may extract a smaller set of reusable facts, decisions, or instructions. Recall is the read side: the system loads or searches stored material and makes selected content available to the model for a later step.
These choices affect what the agent can do. A complete conversation log supports continuity and review, but can be large and include details that are irrelevant to the next task. A compact memory is easier to reuse, but depends on what was selected and may omit context. Frameworks offer different mechanisms and scopes; there is no single universal memory design.
What happens during a working agent’s memory lifecycle
While a task is running
An agent may keep the current conversation and intermediate task data in thread-scoped state. LangGraph describes state as being read at the start of a step and persisted through checkpoints associated with that thread. This supports continuity within a thread; it does not, by itself, mean that the agent has a durable profile or memory shared across other threads.
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When a run ends
An application can preserve run history for later replay, derive candidate memories from the interaction, or do both. OpenAI Agents SDK sessions, for example, retrieve and prepend session history before a run, then store new run items afterward. That is a conversation-continuity mechanism rather than a guarantee that the system has distilled the interaction into reusable lessons. See the OpenAI Agents SDK sessions documentation.
A separate documented OpenAI sandbox-memory pattern illustrates distillation and consolidation: run segments are appended to conversation files, a summary and compact raw memories are extracted, and those are consolidated into a memory index and summary. The sequence is specific to that implementation, not a standard every agent follows. See OpenAI Agents SDK agent memory.
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When a later task begins
Recall might replay full session history, load thread state, inject a short memory summary, search an index, or expose a memory store that the agent can consult. In the OpenAI sandbox-memory example, a short summary is loaded first; when earlier work appears relevant, the agent can search an index and open more detailed summaries. This progressive-disclosure approach avoids loading every detail up front, but it is one documented design rather than a universal retrieval rule.
Other systems draw the boundary differently. LangGraph distinguishes thread-scoped short-term state from long-term memory stored in namespaces that can span threads. Anthropic’s Managed Agents documentation describes workspace-scoped memory stores attached to sessions. LangChain’s LangGraph documentation puts the design trade-off plainly: “Long-term memory is a complex challenge without a one-size-fits-all solution.” See LangGraph memory concepts and Anthropic’s agent memory documentation.
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Common memory scopes and representations
“Memory” can refer to different data with different lifetimes. Before choosing a mechanism, decide what it should remember and who or what should be able to reuse it.
| Design choice | What it means in practice |
|---|---|
| Scope | Information may live for one turn, one thread or session, a user, a project or workspace, or an application. LangGraph documents thread-scoped state and namespace-based cross-thread stores; Anthropic documents workspace-scoped stores attached to sessions. |
| Representation | Memory can be full messages, a summary, structured facts about a user or project, an episodic record of prior actions, or procedural instructions. LangGraph describes semantic, episodic, and procedural memory as useful categories. |
| Recall method | A system can replay a history automatically, load step state, search an index, or let an agent access a memory store through tools. The cited implementations show several of these patterns, not a universally best retrieval algorithm. |
| Update timing | Information can be written during a user-facing run or consolidated afterward. A later consolidation step can separate the conversation record from the smaller set of candidate memories. |
| Correction controls | Memory may support direct edits, stale-item updates, version history, archival or deletion, and provenance indicating where a fact came from. These controls vary by product. |
| Persistence and access | The design must define how data survives restarts, whether agents or sessions share it, and how user or tenant authorization applies to the underlying storage and backups. |
| Context use | Loading everything on every call is different from loading a compact summary and fetching detail only when it is relevant. The cited documentation describes progressive disclosure, but supplies no cross-platform cost benchmark. |
Three documented implementation patterns
OpenAI Agents SDK sessions: preserve conversation continuity
A session retrieves its stored history and prepends it before the next run. Afterward, it stores new run material, including user input, assistant output, and tool calls. The documentation lists backends including SQLite variants, Redis, MongoDB, hosted Conversations storage, and other adapters; the selected backend determines where session data is persisted.
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A session identifier is a selector, not an identity check. OpenAI warns that knowing a session ID does not authenticate a user or authorize access to its history. Applications need to enforce access controls around sessions, databases, and backups. OpenAI Agents SDK sessions documentation.
OpenAI sandbox memory: distill and retrieve reusable context
The sandbox-memory example keeps reusable memory distinct from conversational session history. It uses conversation files, summaries, compact memories, and an index; recall can start with a short summary and open relevant details only when needed. The documentation also describes updating stale memory and advises treating saved memories as guidance while trusting the current environment. Reuse depends on preserving or restoring the configured memory workspace, so saving a memory in a temporary workspace alone does not establish that a later run will see it. OpenAI Agents SDK agent memory documentation.
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Anthropic Managed Agents: attach workspace-scoped memory
Anthropic documents a memory store as a workspace-scoped collection of text documents attached when a session is created. Updates produce immutable versions, supporting an audit trail and point-in-time recovery. The documentation labels the feature beta and specifies the agent-memory-2026-07-22 beta header for memory-store requests. Its stated service limits are 100 kB per individual memory (about 25,000 tokens) and up to 10,000 memories per store; these are product limits, not evidence of memory quality or effectiveness. Check Anthropic’s current agent memory documentation for current API behavior and limits.
LangGraph: separate thread state from cross-thread memory
LangGraph’s short-term memory is state associated with a thread and saved through checkpoints. Its long-term memory uses stores and namespaces to make information available beyond one thread. The documentation also distinguishes semantic facts, episodic experience, and procedural instructions, which helps clarify what a system is trying to retain. LangGraph memory concepts.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to choose a design for a real agent
Start with the task the memory must support, not with a storage product. A useful design makes the scope, write policy, recall path, and correction process explicit.
- Define the boundary. Decide whether the information belongs only to a turn, a thread, a user, or a project/workspace. Keep data isolated when different users or tenants must not share it.
- Choose what to retain. Decide whether continuity requires full messages, or whether later work can use concise facts, decisions, action records, or instructions. A transcript can remain available for audit while a smaller memory serves routine recall.
- Specify when writes happen. Saving during the run can make new information available quickly. Consolidating after the run can reduce noise and produce a more organized memory, but it introduces a delay and another stage that can make mistakes.
- Make recall selective where appropriate. Load the minimum useful context first, then retrieve details when the task calls for them. If the application instead replays full session history, account for the extra context and unrelated material it may carry.
- Build in correction and forgetting. Provide a way to revise or remove wrong, outdated, or unwanted information. Keep provenance or version history when it matters to explain how a memory changed.
- Secure the storage boundary. Authenticate and authorize access at the application and data-store layers. Do not treat a thread, session, or memory-store identifier as permission to read its contents.
- Verify persistence across runs. Test that the chosen backend or workspace is actually restored for the intended user, project, and session. A write that cannot be retrieved later is not useful operational memory.
What memory can and cannot promise
Saved information can be stale, incomplete, or misleading. A system should treat memory as a potentially useful prior, not as a source that automatically overrides current user instructions or the current environment. Updating stale memory is one documented control; immutable versions are another. They address different needs—correction versus auditability—and neither is present in every implementation.
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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchThere is no general effectiveness statistic established by the cited implementation documentation, and it does not justify claiming that a particular memory pattern reliably improves accuracy. The practical question is whether the selected facts are appropriate for the next task, accessible to the right agent, and easy to correct when they are not.
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