Immutable, append-only history.
New experience adds to the record. A correction becomes a new event instead of silently rewriting what came before. The design preserves original events and artifacts, so a summary never has to replace its source.
Memory makesus who we are.
We’re building memory into the fabric of AI agents, so experience can become part of who they are.
Explore Mnemon
Leave a trace.
Memory. Identity. Continuity.
ExploreAn agent’s past
belongs in its future.
We see memory as part of an agent’s inner architecture. A lasting agent needs a way to carry its experience forward: the work it has done, the decisions it has made, and the lessons it has earned.
Our work begins with Mnemon. We’re building a foundation that connects an agent’s history to the context it uses now, so each new task can build on what came before.
Join the early-access waitlistExperience
Conversations, actions, decisions, and corrections form a history worth carrying forward.
Identity
A coherent history gives an agent a basis for understanding its responsibilities, relationships, and the work it is part of.
Continuity
Our goal is for an agent to resume with useful context, even as conversations end and new sessions begin.
Mnemon is our first chapter.
The pillars Mnemon stands on.
A lasting agent needs more than a longer prompt. It needs a trustworthy record, useful views, connected recall, and a way to learn without losing its history.
Record
Connect
Assemble context
Context is a view.
Bring the relevant pieces of a lasting memory into each task. Summaries and working views are derived from the record and can be rebuilt from it. The agent’s current context is a selection from its memory, not the whole memory itself.
Recall follows connections.
Connect events, artifacts, decisions, and their sources. We’re building relationships and rules into the database itself, bringing graph paths, structured filters, and semantic search into a shared memory foundation.
Learning keeps its evidence.
Trace corrections and lessons back to recorded experience. A failed attempt can inform the next one without disappearing from the history. Our design keeps changes to durable knowledge accountable to evidence and human judgment.
Mnemon is under active development. These pillars describe the architecture we are working toward. Our focus is getting the core memory loop right: record experience, derive useful views, and recall what matters.
See what you could buildWhat could you build?
Imagine agents whose next task can build on the last. These scenarios show how the memory architecture could support them.
The right pieces,
for the task at hand.
A memory can hold more than an agent needs right now. Context brings selected pieces together while keeping a path back to the whole record.
A coding agent that picks up the thread.
A fresh session could recall the design decision, failed attempt, and test result behind a change. Imagine resuming a checkout fix with the agreed timeout and the regression test already in view.
Build pattern Record decisions, diffs, failures, and test outputs. Assemble a task view when the next session begins.
A research agent that can show its work.
Follow a conclusion back to its sources and revise the working view when new evidence arrives. Imagine a literature review that can show which paper supported a claim and why a later result changed it.
Build pattern Record source material and evidence links. Derive a review view; append new evidence and corrections as the research develops.
A project agent that remembers why.
Carry decisions and corrections into the next handoff. Imagine a launch assistant recalling that the team moved a deadline, who approved it, and which tasks now need attention.
Build pattern Record briefs, decisions, approvals, and corrections. Derive the current task view, with human judgment governing durable changes.
Illustrative build scenarios, not shipped integrations.
Be part of what comes next.
Join the Mnemon Labs early-access waitlist for project updates and an invitation when access opens.
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