Self-hosted open-source Agent OS with 5-tier memory, 60+ tools, multi-user teams
Best for: Developers building autonomous agents with full control over code and infrastructure, AI researchers needing transparent, customizable agent memory and routing
Per-second billed cloud sandboxes for AI agents with browser and computer use
Best for: AI agent developers needing isolated compute for browser and computer use tasks, Data scientists running code analysis and visualization in the cloud
Open-source agent runtime for persistent, SSH-native Linux boxes per AI agent
Best for: Developers running long-lived coding agents (Claude Code, Cursor) who need a persistent, isolated Linux box, Teams needing secure CI environments with SSH debugging on failure
Authorization layer for AI agents that secures API access with OAuth, policies, and audit.
Best for: Developers building multi-agent production systems that need secure API access, Teams that need full audit trails tracing AI actions back to specific humans
Mount Supermemory as a real filesystem—ls, cat, and grep become semantic memory operations.
Best for: Developers building autonomous agents (Claude, Codex) who want persistent memory without SDKs, Researchers managing large document corpora and needing semantic search across formats
Open, local, YAML-based memory for AI agents — one shared memory for every tool.
Best for: Developers using multiple AI coding agents and want consistent memory, Privacy-conscious individuals seeking local memory storage without cloud
Outcome-weighted memory layer that keeps what worked, not just what sounds related
Best for: Developers building production agents that need to learn from what worked, not just what sounds similar, Teams using LangChain/LangGraph wanting drop-in persistent memory with knowledge graph capabilities
Shared cognition for teams and Claude Code agents—persistent memory stored as a living context graph.
Best for: Development teams using Claude Code daily wanting persistent collective memory, AI-native teams building multi-agent human-AI workflows that need continuity
Real-time Postgres sync and durable HTTP streams for building collaborative, multi-agent systems.
Best for: Developers building collaborative multi-agent systems with durable agent loops, Teams creating real-time local-first applications with Postgres sync
One shared, inspectable memory layer for all your AI agents
Best for: Developers running multiple coding agents who need consistent context across sessions, Teams building autonomous support agents with durable customer context
Local-first AI memory that records your screen and audio for any agent
Best for: Developers building AI agents that need real-time desktop context, Knowledge workers who want to search all meetings, screens, and app activity locally
Mount live enterprise data as a POSIX filesystem for production AI agents
Best for: AI/ML teams needing fast, in-place data access for production agents, Enterprises building legal or research agents with compliance and access controls
