Every conversation with an agent that can't remember feels like starting over. Effective agent memory requires more than storing conversation history—it's about what to store, how to retrieve it, and how to maintain it over time. Explore the inference.sh runtime →
Every conversation with an agent that cannot remember feels like starting over. You explain your preferences, provide context about your project, share information the agent needs - and the next day, it has forgotten everything. Agent memory solves this problem, but implementing it well requires more than just storing conversation history. The difference between useful memory and memory that clutters and confuses lies in how information is stored, retrieved, and maintained over time.
Why Agents Need Memory Beyond Context
Large language models have context windows that hold conversation history during a single session. While these windows have grown substantially - some models now handle over a hundred thousand tokens - they still have limits, and more importantly, they reset between conversations. Context is temporary. Memory is persistent.
The practical implications show up quickly. A customer support agent that cannot remember previous conversations makes users repeat themselves. A research assistant that forgets what it learned yesterday cannot build on past work. A personal assistant that loses track of user preferences feels impersonal despite calling itself an assistant. These are not edge cases. They are the default experience with agents that lack proper memory.
Memory also matters within conversations. Long-running tasks accumulate information that may exceed context limits. An agent researching a topic might gather findings from twenty sources. Summarizing all of that into the context window loses detail. Storing it in memory allows selective retrieval when specific information becomes relevant.
The challenge is that memory is not a simple feature to add. Deciding what to remember, how to organize it, when to retrieve it, and how to maintain it over time involves design decisions that affect agent behavior in subtle ways.
Types of Agent Memory
Different use cases call for different memory approaches. Understanding the options helps in choosing what fits your needs.
Conversation history is the most basic form - storing the sequence of messages exchanged between user and agent. This provides continuity within and across sessions but grows unbounded and can become overwhelming to search through.
Episodic memory stores specific events or interactions as discrete records. The agent remembers that a particular conversation happened, what was discussed, and what the outcome was. This works well for recalling past interactions but requires organizing events in a way that makes retrieval useful.
Semantic memory stores facts and knowledge extracted from interactions. Rather than remembering that a conversation happened, the agent remembers that the user prefers morning meetings, or that the project uses Python 3.11, or that the client's main concern is latency. These facts exist independently of when they were learned.
Working memory holds information relevant to the current task. When an agent starts a research project, working memory might accumulate key findings as the agent progresses. This differs from long-term memory in that it focuses on the active task rather than persistent knowledge.
Most production agents need a combination. Conversation history provides context for recent messages. Semantic memory provides persistent facts. Working memory supports complex tasks. The question is how these are implemented and integrated.
inference.sh includes built-in memory. Key-value storage, conversation persistence, and working memory are part of the runtime. No custom infrastructure required. See how it works →
The Implementation Challenge
Building agent memory from scratch requires solving several interconnected problems.
Storage needs to handle potentially large amounts of data across many users and conversations. A simple key-value store works for basic cases but scales poorly. Vector databases enable semantic search but add operational complexity. The storage choice affects what queries are possible and how fast retrieval operates.
Retrieval determines which memories surface when. An agent cannot review all memories before every response - that would be too slow and would overwhelm the context window. Some mechanism must identify relevant memories based on the current conversation. This is harder than it sounds because relevance depends on context that may itself not be in the retrieved memories.
Summarization becomes necessary as memories accumulate. Raw conversation history from months of interactions would be impossibly long. Effective memory systems compress and abstract older information while preserving important facts. Getting this wrong means losing valuable information or keeping useless detail.
Maintenance handles the lifecycle of memories. Some information becomes outdated - a preference changes, a project ends, a fact is corrected. Memory systems need mechanisms to update, deprecate, or remove information over time. Without maintenance, memory degrades in quality even as it grows in quantity.
Privacy and isolation matter when agents serve multiple users. One user's memories should not leak into another user's sessions. Access control must be enforced at the storage layer. Compliance requirements may dictate retention policies and deletion rights.
Each of these components takes substantial engineering effort. Together they represent a significant infrastructure project before you can focus on the agent behavior that makes memory useful.
A Practical Memory Model
Production agent systems benefit from a simple, well-defined memory model rather than trying to implement every memory type from the research literature. A pragmatic approach focuses on two layers.
The first layer is automatic conversation persistence. The complete message history - user messages, agent responses, tool calls and results - stores persistently and automatically associates with the conversation or user. This requires no agent logic to manage. The system handles storage, and retrieval surfaces recent history as part of the agent's context.
The second layer is explicit key-value memory that the agent controls. The agent can store information with semantic keys and retrieve it later. This memory is:
Explicit - the agent deliberately decides to remember something rather than automatic extraction. This avoids cluttering memory with irrelevant information.
Keyed - information stores under a descriptive key that aids later retrieval. Rather than throwing facts into a pile, the agent organizes what it remembers.
Scoped - memory belongs to a specific conversation or user. Different conversations have different memories. Different users have different memories.
Persistent - memory survives across sessions and restarts. Information stored today is available tomorrow.
This model is simple enough to reason about yet powerful enough to cover most use cases. The agent stores what seems worth remembering and retrieves it when relevant. The infrastructure handles persistence and isolation.
Using Memory Effectively
Having memory available is one thing. Using it well is another. Agent prompts should guide appropriate memory usage.
Good memory practices include storing user preferences when explicitly stated, recording important facts discovered during research tasks, saving context that will be needed across sessions, and noting corrections to avoid repeating mistakes.
Poor memory practices include storing every detail indiscriminately, saving information that is easily re-derived, cluttering memory with temporary task state that does not persist usefully, and forgetting to update memories when information changes.
System prompts can guide these behaviors by explaining what kinds of information to store, when to store it, and what keys to use. Some examples:
For a customer support agent: "Remember user account details, previous issues they reported, and any preferences they mention. Store these under keys like user-account, previous-issues, and preferences."
For a research assistant: "When completing a research task, store key findings under the topic name. This allows building on past research in future sessions."
For a personal assistant: "Note user preferences for scheduling, communication style, and work patterns. Update these memories when preferences change."
The prompts do not need to specify memory mechanics - just what information matters and how to organize it.
Memory and Context Interaction
A common confusion is the relationship between memory and context. They serve different purposes and work together.
Context is the information immediately available to the model during a response. This includes the recent conversation history, any retrieved memories, system instructions, and tool definitions. Context determines what the model knows right now, in this turn.
Memory is the persistent storage from which relevant information can be retrieved into context. Memory is large and grows over time. Context is bounded by the model's limits. Not all memory fits in context, and not all context comes from memory.
Effective retrieval bridges the gap. When a user mentions their project, the system retrieves relevant memories about that project into context. The model can then respond with awareness of stored information. Without retrieval, memories exist but do not influence behavior.
The practical implication is that memory is only useful if it surfaces at the right time. Storing information that never gets retrieved wastes effort. The retrieval mechanism - whether explicit key lookup, semantic search, or something else - determines memory's actual value.
Memory Across Agent Architectures
Different agent architectures interact with memory differently.
Single agents have straightforward memory needs. One agent, one memory scope, direct storage and retrieval. This is the baseline case.
Multi-agent systems introduce questions about memory sharing. Do sub-agents share memory with parent agents? With sibling agents? The answer depends on what the agents are doing. Research sub-agents might accumulate findings in shared memory that the orchestrator later synthesizes. Or they might keep memories separate to avoid confusion.
Persistent agents that serve users over long periods accumulate significant memory. Maintenance becomes important. What from a year ago is still relevant? How do you deprecate outdated information without losing valuable history?
Ephemeral agents created for single tasks may not need persistent memory at all. Working memory during the task might suffice. But if users return to similar tasks repeatedly, some memory between runs improves the experience.
Designing memory strategy requires considering the agent architecture and usage patterns. A one-size-fits-all approach rarely optimizes for any particular case.
Building With Memory
If you are building agents that need to remember, the decision is whether to build memory infrastructure yourself or use a platform that provides it.
Building yourself means implementing storage, retrieval, and maintenance. You get complete control over how memory works but invest significant effort before agents can use it.
Using a runtime that provides memory means the infrastructure exists. You configure how your agent uses it through prompts and possibly some configuration, but the underlying mechanisms are handled.
Either way, thoughtful prompt design determines whether memory improves agent behavior or just adds noise. The technology enables memory; the design makes it useful.
For teams building agents that need to maintain context over time, inference.sh provides built-in memory as part of the agent runtime. Conversation history persists automatically. Key-value memory is available for explicit storage. You focus on what your agent should remember and how to use that information, not on building the storage layer.
FAQ
How much memory can an agent have, and what happens when it gets too large?
Memory systems can store substantial amounts of data - the limits depend on the underlying storage infrastructure rather than fundamental constraints. However, what matters more than total capacity is retrieval efficiency. Large memories with poor retrieval become slow and return irrelevant results. Production systems typically implement maintenance policies that summarize, archive, or remove older memories based on usage patterns. The goal is keeping actively useful information accessible while preventing memory from becoming an unsearchable archive. Systems should monitor memory growth and retrieval performance to identify when maintenance is needed.
Should agents automatically extract memories from conversations, or should memory storage be explicit?
Both approaches have trade-offs. Automatic extraction ensures nothing important is missed but risks cluttering memory with irrelevant information. Explicit storage keeps memory clean but depends on the agent correctly identifying what matters. A hybrid approach often works well: automatically persist conversation history for continuity, but require explicit storage for semantic facts that should persist long-term. This balances coverage with precision. Prompts can guide the agent on when explicit storage is appropriate, providing a middle path between fully automatic and fully manual memory management.
How do I handle memory for agents that serve many users?
Each user should have isolated memory that cannot be accessed by other users or their agents. This is both a privacy requirement and a practical necessity - mixing user memories would confuse the agent and leak information. The infrastructure must enforce isolation at the storage layer, not just the application layer. When designing memory keys, include user or conversation identifiers to ensure uniqueness. Access control should prevent cross-user queries even if someone tries to request another user's memories. Compliance requirements may also dictate how long user memories can be retained and how deletion requests are handled.