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Provide governed graph memory, hybrid RAG, and scoped access control for agents.
Copy the install command and let the AI configure it · recommended for beginners
Please install the "io.github.GCTRL-TECH/gctrl" MCP server from askskill: Run: claude mcp add 'io-github-gctrl-tech-gctrl' -- npx -y gctrl-mcp
Extract key entities, relationships, and facts from these project documents, store them in graph memory, and support later retrieval-based Q&A.
Structured knowledge extraction results stored for later agent retrieval through graph memory.
Fuse knowledge from product docs, meeting notes, and research notes, then answer: "What is the highest-priority requirement this quarter?" using hybrid RAG.
A fused knowledge view and a more complete answer generated with hybrid retrieval.
Create a read-only access token for a support agent limited to the customer support knowledge scope, preventing access to engineering materials.
A scoped access configuration that limits what knowledge the agent can read.
Developers or product teams can give internal agents graph-based long-term memory by extracting and preserving scattered knowledge with governance. This helps reduce context loss and improve later answers.
In research or product workflows, teams can fuse knowledge from multiple sources and answer questions with hybrid RAG. It fits tasks that need broader context than single-document retrieval.
When different agents should only access specific knowledge scopes, scoped access tokens can enforce those boundaries. This helps reduce the risk of overreaching access.
It provides graph-native memory for agents, including knowledge extraction, knowledge fusion, hybrid RAG, and scoped access tokens. Its focus is making agent memory more governable.
Yes. The description explicitly mentions scoped access tokens, indicating scoped access control.
The provided material does not include installation steps, runtime details, or key requirements. Please check the source repository.
Give agents local memory, recall, search, context, and graph traversal tools.
Provide persistent graph memory, semantic search, and traversal for AI agents.
Build graph-based memory and semantic search for LLM applications.
Build a searchable Markdown knowledge graph for hybrid retrieval and AI workflows.
Give AI agents persistent memory, knowledge graphs, recall, and relationship insights.
Run local semantic search and call graph analysis across codebases.