DataMind
An open-source data workspace prototype for AI agents that addresses dynamic data persistence during agent reasoning. It stores information across five layered data surfaces—knowledge base, database, graph, skills, and memory—and employs a dual-agent architecture that separates write and retrieval responsibilities.
Timeline (last 90 days)
DataMind is an open-source prototype project for exploring whether agents need a dedicated data workspace during the reasoning phase.
DataMind has been open-sourced on GitHub at github.com/OpenDCAI/DataMind.
DataMind separates information into five types of data surfaces: KB (Knowledge Base), DB (Database), Graph, Skills, and Memory.
DataMind adopts a dual-agent architecture in which StoreAgent is responsible for writing information and RetrieveAgent is responsible for retrieving relevant evidence.
Different types of information require different storage and retrieval patterns, and using the same mechanism to handle documents and entity relationships often yields poor results.
A single conversation may simultaneously update multiple data surfaces (such as Memory, DB, and Graph).
Automatic writing is harder to design than retrieval, because writing requires the agent to judge the importance of information, categorize it, and avoid contaminating existing data, resulting in a longer decision chain and higher cost of errors.
Separating storage and retrieval makes permissions, auditing, and data quality easier to reason about and control.
DataMind's five data surfaces can be viewed as a further refinement of the semantic memory layer.
All Facts (9)
DataMind's five data surfaces can be viewed as a further refinement of the semantic memory layer.
50%UnverifiedDataMind is an open-source prototype project for exploring whether agents need a dedicated data workspace during the reasoning phase.
50%UnverifiedDataMind separates information into five types of data surfaces: KB (Knowledge Base), DB (Database), Graph, Skills, and Memory.
50%UnverifiedDataMind adopts a dual-agent architecture in which StoreAgent is responsible for writing information and RetrieveAgent is responsible for retrieving relevant evidence.
50%UnverifiedDifferent types of information require different storage and retrieval patterns, and using the same mechanism to handle documents and entity relationships often yields poor results.
50%UnverifiedA single conversation may simultaneously update multiple data surfaces (such as Memory, DB, and Graph).
50%UnverifiedAutomatic writing is harder to design than retrieval, because writing requires the agent to judge the importance of information, categorize it, and avoid contaminating existing data, resulting in a longer decision chain and higher cost of errors.
50%UnverifiedSeparating storage and retrieval makes permissions, auditing, and data quality easier to reason about and control.
50%UnverifiedDataMind has been open-sourced on GitHub at github.com/OpenDCAI/DataMind.
50%