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Build and manage a persistent knowledge graph for AI memory and semantic search.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "open-neural-substrate" yet — see the docs or source repo.
Use open-neural-substrate to build a knowledge graph for the current project, capturing goals, key decisions, technical constraints, and open issues, with searchable thematic relationships.
A structured set of project memory nodes and links for ongoing querying and updates.
Use open-neural-substrate to retrieve past discussions, decision rationales, and affected modules related to the 'user permissions refactor,' then summarize the most relevant context.
Relevant memory entries, their relationships, and a concise contextual summary.
Use open-neural-substrate to consolidate recently added fragmented notes, merge duplicate concepts, enrich relationships, and output the updated knowledge structure.
A cleaner, deduplicated knowledge graph with improved relationships.
Connect a local GraphRAG knowledge base for private offline document retrieval.
Search local Markdown notes offline with hybrid semantic and keyword retrieval.
Lets AI retain conversation memory for more consistent long-term collaboration.
Give AI clients persistent memory with hybrid search and knowledge graph retrieval.
Delegate coding tasks to OpenAI subagents with parallel, cheap, verified orchestration.
Persist, search, and reinject context across Claude Code sessions.