The most frustrating thing about a capable AI assistant is often what happens tomorrow. You return to a project. The decision you explained yesterday needs explaining again. The reason you rejected an approach has disappeared. A useful result exists somewhere in a transcript, but the next conversation starts without it.
Minds gives AI applications a memory beyond the conversation. Keep useful experience, connect it to sources, and retrieve it when the next task begins. That context lives in a dedicated Mind, powered by Akasha. Helm, our conversational operator, helps you create one from a simple request.
Good work should have a next chapter.
Consider a research assistant preparing a market brief. It reads several sources, discovers that two figures describe different time periods, and decides which comparison is useful. The final answer matters. So do the sources, the distinction between those figures, and the decision that made the answer defensible. If the assistant keeps only the final paragraph, much of the work has already been lost.
The next assignment might concern the same company, a revised forecast, or a disagreement with an earlier conclusion. Useful memory makes it possible to return to that context. An application can retrieve the relevant experience, inspect the underlying source, and decide what still applies. Continuity is a practical advantage: the next task can begin with the work that came before it.
A place for experience, knowledge, and context.
Minds distinguishes six kinds of memory. Episodic memory represents what happened. Procedural memory represents how to do something. Resource memory keeps material behind the work. Vault provides a separate interface for sensitive entries. Working memory holds context for the current task. Associative memory recalls patterns from partial cues. These are different roles, and treating them differently helps builders be intentional about what they retain.
A support conversation illustrates the distinction. The conversation and its outcome can become an episode. A troubleshooting guide can remain a resource. A useful resolution can inform a procedure. The facts needed to handle the current request belong in working context. Your integration decides when to write and connect those records; saving a transcript does not automatically produce a reliable playbook.
Knowledge graphs add relationships to that foundation. A fact can remain connected to a person, project, source, and history. Retrieval helps an application find relevant material again. Learning Isolation offers a review boundary for incoming information before the application accepts it. Each capability addresses a different part of the same problem: keeping useful context usable as the work changes.
Meet the engine behind the memory.
Akasha is the engine behind Minds. It brings storage, memory, retrieval, graph, and related cognitive capabilities into an instance that applications can address. AkashaKV is the underlying key-value store: it saves records under named keys and keeps versions available to the engine. Minds is the platform around that instance, handling the account and management experience.
This separation matters. A builder needs to know which interface manages an instance and which interface works with its data. Minds platform APIs cover management concerns such as instance lifecycle and usage. Instance APIs address the Akasha engine. The documentation separates those surfaces, their authentication requirements, and the implementation notes that affect an integration. A feature name should lead to a concrete explanation and an interface you can inspect.
Start with an intention.
Most people do not arrive at a new product thinking in terms of endpoints. They have something to accomplish. Helm is the Minds operator built around that starting point. Ask to create a Mind, and a supported setup flow can bring the choices and fields into the conversation. A structured artifact gives a request a visible shape: the region, the name, the progress, and the reported result.
The direct workspace remains available for detailed inspection and settings. Developers can choose the documented API, SDK, command-line, and agent connection paths appropriate to their environment. Start in conversation, then use the workspace or developer tools when you need more control. Helm currently supports Mind creation; the documentation identifies the interface for each additional operation.
Remembering is a responsibility.
Persistent memory also raises the standard for control. A record needs a source and a scope. A changed fact needs a way to be revised. Sensitive information needs appropriate access controls. Retention and deletion need to be considered before a demonstration becomes a production workflow. A system that remembers more is useful only when the application can decide what should be remembered and how it may be used.
Minds does not turn a stored claim into a true claim. Nor does a graph connection prove that a conclusion is correct. Evidence, review, and careful integration still matter. We want those decisions to be visible in the product and understandable in the documentation, so that continuity can support judgment rather than conceal it.
Build something that can pick up where it left off.
Start with one useful workflow. Keep a decision with its source. Recall the outcome of an earlier task. Give an agent the procedure it should follow and the context that explains when it applies. Then inspect what was stored and what came back. That small loop reveals more than a promise about remembering everything.
Our ambition for Minds is simple: help AI work accumulate value over time. Models will change. Interfaces will change. The experience that makes your application useful should have a home of its own. Every agent needs a mind.