Local-first persistent memory for AI coding agents via MCP.
Best for: Developers using AI coding agents who want persistent context across sessions, Teams working with multiple agents who need shared memory on the same machine
Postgres, RAG, and agents in one managed backend for AI apps.
Best for: Developers using AI coding agents (Claude Code, Codex, Replit) to build apps rapidly, Teams building AI-native applications that need RAG and agent capabilities
Local-first permanent memory for all LLMs. Every session picks up where you left off.
Best for: Developers using multiple AI coding assistants who want unified memory, Power users who want private, permanent memory for personal LLM interactions
Self-evolving memory OS for AI agents that learn from experience.
Best for: Developers building AI agents requiring long-term, self-evolving memory, Enterprise teams needing multi-agent coordination with shared context
Open-source CLI for running multiple AI coding agents in parallel with local sandbox isolation.
Best for: Developers running multiple AI coding agents in parallel who need sandbox isolation to avoid conflicts, Teams that want to keep code on-premises and avoid cloud AI services for privacy or policy reasons
Local-first persistent memory for AI agents with zero LLM calls — Hebbian learning, offline, free.
Best for: Developers building autonomous AI agents needing deterministic memory, Robotics engineers requiring offline, persistent memory for ROS2/Zenoh platforms
Organizational shared memory that captures and recalls knowledge across all your AI agents and humans.
Best for: Teams running multiple MCP-compatible AI coding agents (Claude Code, Cursor, Codex), Support organizations needing consistent agent answers across reps
