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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 persistent graph memory, semantic search, and traversal for AI agents.
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.