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Agent memory is useful information an AI agent can retrieve on a later run and use to shape what it does. A transcript or log records what happened; it becomes memory in the practical sense only when a relevant lesson is retained and made available to the agent again.
What agent memory means
Memory is not simply a large conversation window or a database attached to an agent. It is application-managed context that can persist, be retrieved when relevant, and influence later behavior. LangChain explains the distinction in How to Build Memory into AI Agents: “A trace, transcript, or log is useful evidence of what happened. It becomes memory only when the relevant lesson is converted into context the agent can retrieve on a later run and use to change its behavior.”
This distinction matters because retaining every interaction is usually neither necessary nor helpful. Run history can be evidence for later review, while memory should contain a smaller amount of useful context. A vector database may be one way to store or search that context, but the database by itself does not decide what is worth remembering, when to retrieve it, or how the agent should use it.
Two ways to classify agent memory
Memory categories are practical design tools, not one universally agreed technical standard. It helps to classify them along two axes: how long or widely the information is available, and what kind of information it contains.
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By scope: working and long-term memory
- Short-term or working memory is context available during the current task or conversation thread. LangGraph describes this as thread-scoped memory: it can be saved as agent state so the same thread can resume later.
- Long-term memory persists beyond one thread or run and can be made available across threads. It might hold a durable preference, a prior outcome, or a standing instruction, subject to access controls and retrieval rules.
These terms describe scope, not a particular storage technology. Thread state may be persisted, and long-term information may be stored in a profile, a set of documents, or another application-defined format.
By content: semantic, episodic, and procedural memory
- Semantic memory stores what the agent or application has established as useful knowledge: facts, user preferences, or other relatively stable information.
- Episodic memory preserves experiences, interactions, examples, or outcomes that may help with a similar situation later.
- Procedural memory records how the agent should act, such as instructions, workflows, policies, or tool-use rules.
The two axes can overlap. A current thread can contain episodic context about the task underway, while long-term storage might hold semantic facts or procedural rules. The useful question is not which single label applies, but what the information is for, how long it should remain available, and when it should be retrieved.
How to add memory to an AI agent
A reliable design separates evidence, memory writing, and retrieval. Start with the behavior the application needs to improve; do not promote every message into a permanent record.
- Capture run history as evidence. Keep traces or conversation history where appropriate so you can understand what happened. Treat that history as a source from which useful lessons may be selected, not as an instruction to retain everything indefinitely.
- Choose durable signals. Identify facts that are likely to remain useful, stable preferences, successful examples, repeated corrections, and workflow rules. Leave one-off details and most routine exchanges in history unless the application has a reason to preserve them.
- Write and maintain memory. Extract or consolidate selected information, reconcile it with existing entries, and provide a way to correct or remove stale or unwanted items. LangMem describes memory operations that use conversations and current memory to expand or consolidate the stored state.
- Retrieve relevant context at runtime. Make the information available through prompt assembly, retrieval, tools, files, or runtime state. Choose a retrieval method suited to the information: a known profile field may be directly read, while a collection of past examples may need search or filtering. Stored content cannot affect behavior if the agent cannot access it.
- Review outcomes and revise. Use user feedback and recurring outcomes to decide whether a memory is still accurate and useful. Update instructions, facts, or examples when behavior or circumstances change.
Before implementation, decide what may be retained, who may access it, how users can correct or delete it, and how the system will identify outdated information. The OpenAI Agents SDK documentation notes that memory artifacts can include conversation content, making sensitivity and retention policy part of the design rather than an afterthought.
Agent memory tools and implementation patterns
The tools below illustrate different approaches; they are not a ranked list, and their documentation does not establish a universally best design or comparative performance results.
| Approach | Scope and representation | How it is used | Main design trade-off |
|---|---|---|---|
| LangGraph | Short-term state can be persisted with thread-scoped checkpoints. Long-term memory can be shared across threads through stores and namespaces. | Use a profile/schema for structured information, or a collection of memory documents for multiple records. Retrieval can use namespaces and search. | A profile is straightforward to retrieve and suits known, well-scoped information, but requires anticipating the schema and can overwrite older information. A document collection can retain many records but is more complex to query, update, and reconcile. |
| LangMem | Memory is application-specific and can be represented as information to extract, update, remove, or consolidate. Its stateful integrations use LangGraph storage primitives. | Memory operations can consolidate conversation information with current memory. Recall can account for more than semantic similarity, including importance and recency or frequency. | Richer selection and maintenance logic can help match an application’s needs, but the application still has to define what matters and how conflicts, access, and deletion work. |
| OpenAI Agents SDK sandbox memory | A sandbox-agent capability for distilling lessons between sandbox runs, implemented with workspace files, a summary or index, and consolidation. | Reuse depends on preserving and reusing the configured memory directory or session/snapshot state. Session history is distinct from this cross-run memory capability. | A fresh empty sandbox starts without that memory. This documented feature is specific to the SDK’s sandbox setup and should not be assumed to apply to every OpenAI agent configuration. |
Read the implementation details in the LangGraph memory concepts and LangGraph memory guides, the LangMem conceptual guide, and the OpenAI Agents SDK sandbox guide and Sessions documentation.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to choose a memory design
Choose the simplest design that gives the agent the needed context with acceptable precision, recall, latency, and maintenance cost. Compare approaches against the actual information and workflow rather than choosing a storage type first.
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Quick Recap
- Scope: Does information belong only to one task or thread, or should it be available across runs? If it is shared, define whether the boundary is a user, team, organization, or application.
- Content: Are you storing facts and preferences, past examples and outcomes, or instructions for behavior? Different content may need different review and update rules.
- Representation: A profile or schema works when fields are predictable. A document collection or files can accommodate many records or less rigid content, but may require more involved retrieval and reconciliation.
- Retrieval: A direct lookup can suit known fields; namespaces, metadata filters, or search can narrow a larger collection. Retrieval should provide relevant context without crowding the prompt with unrelated material.
- Write timing: Memory may be updated during the main run or in a later consolidation step. Immediate writes can reflect new information promptly; deferred consolidation can give the system a chance to combine and reconcile material.
- Maintenance and isolation: Plan for conflicts, corrections, stale facts, deletion, and access boundaries. Namespace or otherwise isolate records so one user or organization does not receive another’s context.
- Operating costs: Consider precision, recall, context length, latency, and the complexity of querying and updating. The right trade-off depends on the task and is not established by a universal benchmark.
Common memory design mistakes
- Calling every log a memory: A stored event has little behavioral value unless the system can select it, retrieve it later, and apply it appropriately.
- Keeping too much: Unfiltered history can add noise, increase retrieval burden, and preserve details that should not become durable context.
- Assuming similarity search is enough: Relevance may depend on importance, recency, frequency, exact metadata, or a known profile field—not just semantic similarity.
- Ignoring lifecycle and privacy: Retention, sensitivity, access, correction, and deletion should be built into the design, especially when memory may contain conversation content.
- Assuming a storage component supplies a memory policy: The application still needs to decide what gets written, how it is scoped, and how the agent consumes it.
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