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Give LLMs persistent semantic memory with concept storage, linking, and retrieval.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "Neural-Stimulus" yet — see the docs or source repo.
Use Neural-Stimulus to store the following user preferences as retrievable concepts and link them: The user is Lin Chen, prefers concise answers, is building a travel planning assistant, and commonly uses Next.js and Supabase.
Returns stored concept nodes and their links so future conversations can use long-term memory.
Retrieve the existing concepts most related to "travel planning assistant" and "Supabase" from Neural-Stimulus, rank them by relevance, and explain their relationships.
Outputs a list of related concepts, similarity results, and relationship explanations from the concept graph.
Write this meeting outcome into Neural-Stimulus: the project goal is to improve customer support auto-reply accuracy; next week's tasks include organizing FAQs, evaluating vector retrieval quality, and testing 256-dimensional embeddings. Link it to the "customer support bot" project.
Creates reusable project memory entries linked to the relevant project and task concepts.
Persistent knowledge-graph memory for MCP with semantic search and version tracking.
Give AI knowledge-graph memory with cloud persistence and semantic search.
Lightweight vector memory for AI agents to store, search, and delete memories.
Provide long-term memory storage and fast semantic retrieval for AI applications.
Build a semantic graph from project files for search, knowledge, and task management.
Provides persistent graph memory for LLMs with auto-linking and layered recall.