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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsAgent memory is information an AI agent can retrieve later and use to guide what it does—not simply a transcript of what it has already done. To add it, decide what is worth retaining, store it in a form the agent can access, retrieve it when relevant, and provide a way to correct or remove outdated information.
What agent memory means
A transcript, trace, or log records what happened. In practical agent design, that record becomes memory when a useful lesson is selected, retained, and made available to influence a later run. LangChain describes memory as durable context that can be retrieved across runs in its guide to building memory into AI agents.
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For example, an agent might log a conversation in which a user repeatedly asks for concise answers. The log is history; a durable preference such as “prefers concise responses” is memory if the agent can retrieve and apply it in a future interaction. Saving everything is not the goal: most run history is evidence, not a useful instruction or fact to carry forward.
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What types of agent memory are there?
There is no single universally accepted taxonomy. Two complementary classification axes help clarify what a system is storing and when it can use it.
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Scope: working memory and long-term memory
- Short-term or working memory is context available during the current task or thread. LangGraph calls this thread-scoped memory: state associated with a thread can be persisted at checkpoints and used to resume it.
- Long-term memory persists beyond a single run or thread and can be retrieved later. LangGraph describes this as cross-thread memory, typically stored separately and organized so an application can retrieve relevant information.
These categories describe availability over time, not what the information means. A live thread can contain experiences from the current interaction, while a cross-run store can contain durable facts or behavior rules.
Content: semantic, episodic, and procedural memory
- Semantic memory stores facts and preferences, such as a user’s preferred units or an organization’s product details.
- Episodic memory stores experiences, examples, interactions, or outcomes that may help the agent handle a similar situation later.
- Procedural memory stores how the agent should act: instructions, workflows, policies, and tool-use rules.
LangChain and LangMem use these categories as practical concepts adapted from cognitive-science terminology, not as a binding technical standard. Scope and content can be combined: a current thread may hold working context about an episode, while long-term storage holds a distilled lesson from that episode.
How to add memory to an AI agent
Build memory as a controlled read-and-write cycle. A store that the agent never consults has no effect; unrestricted storage can preserve noise, sensitive details, or outdated instructions.
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- Capture evidence. Retain run history or traces in a way that fits your application. Treat these records as evidence of what happened, not automatically as durable memory.
- Select durable signal. Promote only information likely to help later: stable facts, explicit preferences, repeated corrections, useful examples, or reliable workflow rules. Decide which details are unnecessary or too sensitive to retain.
- Choose a representation and write policy. Store the selected information as a structured profile, separate memory documents, files, or other application-defined state. Decide whether the agent writes during the main run or whether a later consolidation step updates memory.
- Retrieve context at the right time. Make relevant entries available through prompt assembly, direct lookup, search, tools, or runtime state. Filter by an appropriate user, organization, or application scope; do not rely on a store being globally safe by default.
- Review and maintain it. Provide a route to correct or delete entries, reconcile conflicting updates, and recognize facts that may have gone stale. Use recurring outcomes and feedback to revise instructions or examples.
LangMem describes a consolidation operation that takes conversations and current memory, uses a model to expand or consolidate the stored state, and returns an updated version. Its guide also discusses recall signals beyond semantic similarity, including importance and recency- or frequency-based strength. These are design options, not evidence that one retrieval formula is best for every agent.
Which memory approach should you choose?
There is no universally best storage design in the documentation covered here, and the sources do not provide comparative performance benchmarks. Choose based on what the agent needs to remember, how it will retrieve information, and how you will maintain and isolate it.
| Approach | Useful for | Representation and retrieval | Trade-off to consider |
|---|---|---|---|
| Thread-scoped state in LangGraph | Context needed to continue a particular thread or task. | Agent state persisted through thread-scoped checkpoints. | Supports resuming a thread; it does not by itself make that context cross-thread memory. |
| Long-term store in LangGraph | Information intended to be available across threads. | Store records organized with namespaces; applications can retrieve by lookup or search and filter using their chosen design. | Cross-thread access requires deliberate namespace and access-control design. |
| Profile or schema | A small, known set of durable facts or preferences. | A structured, continuously updated profile designed for direct retrieval. | Can be precise and easy to retrieve, but requires anticipating the schema and can overwrite older information. |
| Memory-document collection | Many records accumulated over time, including experiences or examples. | A collection of documents retrieved using application-defined search or filters. | Can retain more varied information, but querying, updating, and reconciling records is more complex. |
| LangMem operations | Extracting, updating, removing, or consolidating memories. | Memory operations can work with current memory and conversation content; stateful integrations use LangGraph storage primitives. | Memory selection and recall remain application-specific; the guide does not establish a universal configuration. |
| OpenAI Agents SDK sandbox memory | Distilling lessons for reuse between runs of a sandbox agent. | Workspace files, a summary or index, and a consolidation process. | It is a sandbox-scoped capability, not a general claim about every OpenAI agent setup. Reuse requires preserving and reusing the configured memory directory or session/snapshot state; a fresh empty sandbox starts without that memory. |
LangGraph discusses choices such as updating a profile or adding documents, retrieving by search or direct access, and writing in the main run or a background step in its memory concepts documentation and memory how-to guides. LangMem explains its memory operations and application-specific design in its conceptual guide. The OpenAI Agents SDK sandbox documentation and session documentation distinguish sandbox memory from conversational session history.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What to decide before storing user or business information
Memory design is part of an application’s data-handling policy, not just a database choice. The OpenAI SDK documentation notes that memory artifacts can include conversation content, so choose a sensitivity and retention policy appropriate to the information being processed.
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- Access: Scope records to the right user, organization, or application. A namespace is useful only when the application enforces appropriate boundaries.
- Correction and deletion: Make it possible to change or remove a stored fact rather than leaving stale or incorrect information in circulation.
- Conflicts and freshness: Decide how newer information interacts with older entries, and whether a fact should expire or be confirmed before use.
- Retrieval and cost: Balance precision and recall against the context length, latency, and query or update complexity your application can tolerate.
For details on the SDK’s sandbox artifacts and session history, consult its sandbox guide and sessions guide. The relevant documentation describes implementation concepts; it does not establish one storage pattern as the best choice for every agent.
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