$ loading_
Manage LLM memory with multiple memory types and communication protocols.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "ThinkMem" yet — see the docs or source repo.
Using ThinkMem, design a memory strategy for this customer support agent: separate user profile, issue history, preferences, and temporary session context, and explain how each should be written, updated, and retrieved.
A structured memory design with memory types, access rules, and retrieval logic.
Use ThinkMem memory capabilities to plan a multi-turn conversation system so the model remembers user goals, completed steps, pending tasks, and key constraints, while avoiding storing short-term information permanently.
A conversation memory workflow defining short-term versus long-term memory boundaries and update policies.
I am building an LLM-based research assistant. With ThinkMem, design a memory retrieval strategy: when to read past memory, how to archive by topic, and how to reduce irrelevant memory interference.
An actionable retrieval and archiving plan for more reliable use of historical information.
Give AI agents persistent memory, shared reasoning, and auditable collaboration.
Share memory, preferences, and chat history across AI assistants.
Store and query namespaced key-value memory for persistent agent context.
Give AI agents persistent memory and semantic retrieval across conversations.
Provide shared cross-session memory storage, retrieval, and governance for MCP AI tools.
Provides persistent graph memory for LLMs with auto-linking and layered recall.