Memory infrastructure for AI products, agents, and workflows

Give agents durable memory across tasks. Store decisions, tool results, workflow state, and past actions so agents can continue work without stuffing every step into the context window.

Use AgentFS to give agents a structured workspace for plans, task state, memory files, retrieved context, and durable execution notes.

Remember architecture decisions, repository conventions, preferred tools, dependencies, and developer preferences so users do not have to explain the same project context repeatedly.

Preserve architecture decisions, pull request context, debugging notes, deployment lessons, incident summaries, API assumptions, and project-specific conventions.

Maintain structured customer and ticket context over time. Summarize long histories into archival memory, then recall only the issues, decisions, and resolutions relevant to the current request.

Track learner progress, mastered concepts, weak areas, and teaching preferences. Use memory to adapt explanations, practice paths, and review sessions over time.

Store project knowledge, policies, workflows, decisions, team notes, operating procedures, and reusable answers so employees can retrieve practical context quickly.

Store product decisions, release notes, feature requirements, roadmap context, user feedback, and documentation history for guides, changelogs, and API references.

Remember account preferences, onboarding status, open issues, promised follow-ups, product usage patterns, and previous resolutions.

Store sources, summaries, assumptions, findings, open questions, comparisons, and conclusions so research can continue across sessions.

Support automation systems that need persistent state across steps, including completed actions, pending tasks, tool outputs, approvals, and checkpoints.

Act as shared memory for plans, handoffs, results, task ownership, decisions, and retrieved context so multiple agents remain coordinated.