Long-Term Memory for AI Agents That Don't Start Over
Keep the context. Continue the work.
Add long-term memory for AI agents with EverOS. Preserve user preferences, meaningful episodes, and agent experience beyond the current session. Retrieve what matters for the next task instead of replaying the entire history in every prompt.
Let the next session pick up where the last one left off
Your agent may understand a conversation perfectly while that conversation remains in context. A new session can still lose the preference, the earlier decision, or the background that made the answer useful.
Long-term memory for AI agents gives that information somewhere to live beyond a single run. We extract structured memory from interactions, organize it, and make it available for later retrieval. The model uses the returned context to respond; it does not need every previous message pasted into its input.
Keep the right information, not every token
A raw log records what was said. Useful memory represents the information the agent should be able to recover: a preference, an event, a project constraint, or experience from a completed task.
That is the purpose of long-term memory for AI agents in EverOS. You can retain meaningful context without making the full transcript the default input for every future response. The memory still needs to be relevant to the current task and attributed to the right owner.
A context window is working space
The current context window holds the information available to a model during a request. Making it larger does not by itself create a durable system for extracting, correcting, and recalling information across sessions.
Long-term memory for AI agents sits outside that working space. Retrieval brings selected context back into the prompt when it is useful. Persistence and relevance are separate jobs: a fact can be stored correctly and still be irrelevant to the question you are asking now.
The memory types your agent can work with
EverOS keeps User Memory and Agent Memory as separate tracks. This makes the distinction between personal context and execution experience explicit.
Profile: who the user is
Profiles contain persistent user attributes and preferences. They can help an assistant apply context such as a preferred style or a relevant background detail without asking the same question in every conversation.
Use long-term memory for AI agents to preserve these attributes deliberately. A one-off request is not always a standing preference. Information that guides recurring work should remain something you can inspect and correct.
Episode: what happened
Episodes represent meaningful interactions and events. We extract narrative context and atomic facts rather than relying only on undifferentiated message history.
An episode can preserve the discussion behind a decision. Its facts can help the agent recover a specific detail. This gives long-term memory for AI agents a way to represent both the event and the information within it, rather than forcing every question through a full transcript.
Case: how the agent handled a task
Agent Cases capture execution experience. They belong to the agent track, not the user profile. The approach to a debugging task and a person's writing preference are different kinds of memory and should be handled as such.
Cases can inform later work when the experience is relevant. They are evidence from a run, not automatic permission to repeat its actions in a different environment.
Skill: a reusable approach
Agent Skills generalize useful experience from Cases. They support procedural reuse, while profiles and episodes support continuity about users and interactions.
Long-term memory for AI agents can therefore do more than remember facts. It can provide a foundation for retained experience as well. Skill extraction and reflection depend on the configured deployment capabilities; they are not implied by simply storing a message.
How long-term memory for AI agents fits into your loop
Start with a small, explicit cycle: search for relevant memory, generate with that context, and add the new interaction. Our API and SDK expose the operations needed to build that cycle into your existing application.
Search before generating
Query the memory for the current task and the correct identity. EverOS separates user-owned episodes and profiles from agent-owned Cases and Skills. Choose the user or agent track that matches what you need to retrieve.
Use the returned episodes, summaries, facts, and profiles as appropriate context. Long-term memory for AI agents should not require the model to treat every stored detail as relevant. If the search returns no useful memories, continue without inventing background.
Generate with selected context
Your LLM receives the task and the retrieved information. This is where memory becomes useful: the agent can reason with earlier context while still following the current instruction.
Keep that distinction explicit in your application. Long-term memory for AI agents is an input to reasoning, not a replacement for it. A remembered plan may be outdated; a current correction may take precedence over a previous preference.
Add the new interaction
After the response, add the interaction with consistent session and sender identifiers. Attribution matters because the system needs to know whose information it is processing and which interaction it came from.
In our Cloud API, writes are asynchronous by default. An acknowledgement means processing has been queued; it does not mean all extracted memories are immediately searchable. Your integration should handle that timing instead of assuming storage and retrieval happen simultaneously.
Close the session when it matters
The latest messages can remain buffered until a later add, a flush, or the Cloud session's idle closure. Use flush when a conversation ends and you need extraction, after the write has actually landed.
This is a practical detail of long-term memory for AI agents: extraction and indexing have their own lifecycle. Get can see an extracted memory before vector search finishes indexing it. An immediate empty result is not necessarily evidence that the interaction was lost.
Persistent agent memory with selective retrieval
EverOS provides keyword, vector, hybrid, and agentic retrieval. Choose the method that fits the query and the capabilities you have configured.
Keyword search for known terms
When you know the phrase or identifier you need, keyword search provides a direct way to find matching memory. It is also available in the minimal self-hosted configuration with the core memory flow.
Not every request needs semantic inference. Exact terminology can be the right starting point for a project name, a known phrase, or a specific fact the user previously supplied.
Vector and hybrid search for meaning
A question may describe the same idea in different language. Vector retrieval addresses semantic similarity; hybrid retrieval combines keyword and vector approaches.
For long-term memory for AI agents, this matters when the current question does not repeat the wording of an earlier interaction. Self-hosted vector and hybrid capabilities require the appropriate provider configuration. Do not request a mode the deployment has not enabled.
Agentic retrieval for harder questions
Agentic retrieval is available for more complex, multi-part searches. It is not a reason to make every simple lookup a more elaborate process.
Start with the simplest retrieval mode that serves the task. Check the relevance of the result, not just that a search returned something. Memory is useful when the agent gets sufficient context to answer correctly, not when it receives the largest possible set of records.
Memory and RAG solve related, different tasks
Retrieval-augmented generation can bring external information into a model's response. A document knowledge base is useful when the task is to find information in a collection of material.
Long-term memory for AI agents adds the continuing interaction lifecycle: user attributes, meaningful episodes, agent experience, and context that evolves as the system is used. The difference is not simply which database stores the text. It is what the information represents and how it changes over time.
You can use document retrieval and personal memory together. A policy document can provide the current rule; a user profile can provide the person's relevant preference. The application should distinguish those sources instead of treating every retrieved passage as the same kind of evidence.
Keep long-term memory for AI agents correctable
A durable mistake is still a mistake. Preferences change, old plans expire, and an extracted fact may be attributed to the wrong subject. Memory needs control after it has been written.
In EverOS Cloud, you can correct an episode, edit an extracted profile, delete specific memories, and record feedback. The memory console also lets you inspect stored profiles, episodes, Cases, and Skills by owner and type.
Correct a fact at its source
Use the operation appropriate to the memory type. Changing one episode and editing a user profile are distinct operations. A profile edit requires that profile extraction has already produced a profile.
Feedback records a signal; it does not itself rewrite the memory. Long-term memory for AI agents should therefore be paired with a deliberate correction path, rather than assuming a negative rating has immediately fixed the underlying information.
Keep deployment differences visible
Cloud and self-hosted EverOS share core memory operations, but every lifecycle endpoint is not identical in availability. Our current self-hosted runtime supports add, flush, get, and search; several API correction and feedback operations are Cloud-specific.
The local runtime provides readable Markdown and indexes that synchronize with file changes. Choose the control surface that matches your deployment. Do not build a self-hosted workflow around a Cloud-only endpoint without checking its availability.
Connect cross-session memory to your existing agents
Our ecosystem includes Claude Code, Codex, OpenClaw, Hermes, and MCP integrations. You can add memory to an existing host or connect your own application through the supported API and SDK.
Long-term memory for AI agents does not require replacing the agent's entire workflow. It does require choosing consistent identities, defining when to store interactions, and deciding how retrieved context enters the prompt.
Use Cloud when you want managed infrastructure
Cloud lets you connect to our managed memory service without operating the memory runtime. Configure your application and integration, then verify the add-and-retrieve cycle with a small interaction before expanding it.
Self-host when you want control of the runtime
Our open-source stack stores Markdown alongside SQLite and LanceDB. You configure the model providers and the capabilities needed for your workflow.
Self-hosted does not automatically mean fully offline. External extraction, embedding, or other provider calls depend on your configuration. Multimodal ingestion also requires the relevant parser and model setup; office-file parsing requires its system dependency.
Check continuity before expanding the system
Start with a simple, verifiable interaction. Add a non-sensitive preference, let extraction complete, and retrieve it in a later session. Then test a correction and an identity boundary.
For long-term memory for AI agents, check:
- The right user receives the right context in a fresh session.
- A different user does not inherit that personal context.
- Retrieval finds a relevant fact when the question uses different wording.
- Your integration accounts for write, extraction, and index timing.
- An outdated fact can be corrected through the supported control surface.
- The agent follows the current request even when an old memory differs.
These are practical acceptance checks, not benchmark promises. Run them against your actual application, model, and deployment. A memory demo becomes useful infrastructure when its behavior is predictable enough to build on.
Frequently asked questions
What is long-term memory for AI agents?
It is durable context that an agent can retrieve beyond its current session. EverOS extracts and organizes user profiles, episodes, and agent experience, then returns relevant memory for later tasks. The agent uses that context without needing the entire interaction history in every prompt.
Does long-term memory for AI agents replace the context window?
No. The context window remains the model's working input. Memory persists outside it and supplies relevant information through retrieval. Your application combines the current task with selected context rather than expecting persistent storage to replace the prompt.
How does persistent agent memory differ from a chat log?
A chat log preserves messages. EverOS extracts structured memory, including episodes, facts, and profiles, and separates user context from agent Cases and Skills. That structure supports selective recall rather than requiring the agent to reread every message.
Can cross-session memory work with existing coding assistants?
Yes. Our ecosystem includes Claude Code, Codex, OpenClaw, Hermes, and MCP integrations. Setup depends on your host and deployment. Configure the appropriate integration and verify that the correct identity is used for writing and retrieving memory.
Is long-term memory for AI agents immediately searchable after a write?
Not always. Cloud writes are asynchronous by default, recent messages can remain buffered, and vector indexing can lag extraction. Use the documented write and flush lifecycle. A queued response is not a guarantee that every search method can return the new memory immediately.
Can I correct or remove persistent memory?
In Cloud, we provide profile editing, episode correction, deletion, and feedback operations. Our self-hosted runtime has different API availability and readable Markdown-backed memory. Use the supported controls for your deployment; recording feedback alone does not automatically rewrite a memory.
Give your agents a continuing context
Start with one add-and-retrieve loop. Verify identity, timing, and relevance. Then bring long-term memory for AI agents into the workflows where starting over is costing you useful context.
