MemoryRouter - 2026 Pricing, Features, Reviews & Alternatives

MemoryRouter is persistent memory infrastructure for AI tools, agents, and the products you build. It keeps one portable memory profile that follows you across ChatGPT, Claude, Cowork, Claude Code, Codex, OpenClaw, DeepSeek Harness, and any MCP-compatible client, so context built in one tool is available in every other tool you connect.

How it works: a two-minute transfer moves what ChatGPT already remembers about you into your own vault. From there, each connected assistant reads relevant context on demand and saves new memory back to the same vault, under your review. You approve what gets saved, and you can search, edit, or delete anything at any time. The result is consistent context across assistants: preferences, projects, decisions, and background stay current no matter which AI you open next.

For developers and product teams, MemoryRouter acts as a memory layer with a hosted MCP endpoint and a memory API. Proxy mode handles retrieval, prompt injection, model calls, and storage in one round trip; local inference mode lets your app call /memory/prepare and /memory/ingest around your own model calls. Every user gets a private vault scoped to their Memory Key, with usage-based pricing that follows memory activity rather than seats.

Integrations include ChatGPT, Claude and Cowork, Claude Code, Codex, OpenClaw, DeepSeek Harness, any MCP client, a CLI, and a public SDK on GitHub. Typical recall runs in 300 to 500 milliseconds on a typical vault.

Security: memory is encrypted in transit (TLS 1.2+) and at rest (AES-256), each vault is isolated at the retrieval layer with one key per vault, API keys are hashed before storage, and deletion is a hard delete. Memory is never shared or trained on.

Pricing: $20 per month after a 14-day free trial, including 200 million memory tokens per month with no inference markup; extra usage is $0.50 per 1 million tokens. Enterprise options add shared team memory and custom data residency.