Persistent state for AI clients
Writing on durable knowledge, task state, MCP, and external runners that use the same record across clients and sessions.
What is an MCP memory server?
An MCP memory server gives AI clients durable context. Learn what MCP does, what the server stores, and how to choose a record you can correct.
Read →Basic Memory alternatives: which record do you need?
Considering a Basic Memory alternative? Choose local Markdown and a graph, browser-first notes, hosted team knowledge, or durable task state for AI work.
Read →Assigned tasks for AI agents: state, not a queue
Assigned tasks for AI agents need a shared, inspectable record. See what a task should contain, what a runner decides, and how handoffs stay clear.
Read →What is a knowledge layer for AI?
What is a knowledge layer? It is a durable record of context and work that helps AI clients begin informed, share updates, and survive new sessions.
Read →What to store in AI memory (and what to leave out)
What to store in AI memory: save preferences, decisions, procedures, and live work. Leave chat debris behind so fresh sessions get useful context.
Read →How to share context between AI tools
Share context between AI tools without rebuilding your brief in every chat. Keep one readable record for Claude, Cursor, and ChatGPT apps that support MCP.
Read →Best MCP memory servers compared
The best MCP memory server depends on the record: compare Basic Memory, Hjarni, Mem0, Notion, local options, and vtriv for context and task records.
Read →Persistent memory for long-running AI agents
Long-running AI agents use separate sessions. Persistent memory and task records give an external runner clear handoffs between them.
Read →Mem0 alternatives for shared AI memory
Compare Mem0's hosted MCP server and OpenMemory with Zep, Letta, and vtriv for shared AI memory, readable context, and durable task records.
Read →CLAUDE.md at scale: when one file stops working
CLAUDE.md is right for one repo. It runs out across twelve repos, two machines, and clients that never read the file. What to keep, and what to move out.
Read →A ChatGPT memory alternative you can read
ChatGPT's memory lives inside one product. vtriv keeps your context in plain markdown; ChatGPT access depends on your plan and MCP permissions.
Read →How to give Claude persistent memory across sessions
Claude's chat memory has plan and Cowork boundaries. vtriv keeps portable, authored Markdown context and task state for shared MCP access.
Read →The cost of starting over
AI chat memory helps, but it doesn't create a portable project record. Keep decisions, instructions, and work context in a durable layer.
Read →What's worth keeping for your AI
Once your assistant can read a context layer, the real question is what to store in AI memory. Four kinds that earn their place, and what to leave out.
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