I use Neo4j a lot with my AI agents, letting them store private memory as a knowledge graph, but also research this graph in auto-scientific process. I've discovered that reducing cognitive load on an LLM is crucial for the quality of machine reasoning. And this is intention behind this library - no explicit "async", DSLs for idiomatic resource management, automatic mapping of Cypher input and output data to multiplatform data classes. All of this can be executed as a script, while being strongly typed and compiled giving additional feedback to the autonomous chain-of-code style agent. This allows agents to define ad hoc data ingestion and retrieval schemas, while avoiding double-task inference challenge of encoding intents while comprehending own intents.
0 comments
—
Show HN: Neo4j SDK with minimal cognitive load for an LLM · HackerTrans
https://github.com/xemantic/xemantic-neo4j-kotlin-driver
There is also associated demo project showing how to use this driver with Ktor, in fullstack asynchronicity and structured concurrency of coroutines:
https://github.com/xemantic/xemantic-neo4j-demo
I use Neo4j a lot with my AI agents, letting them store private memory as a knowledge graph, but also research this graph in auto-scientific process. I've discovered that reducing cognitive load on an LLM is crucial for the quality of machine reasoning. And this is intention behind this library - no explicit "async", DSLs for idiomatic resource management, automatic mapping of Cypher input and output data to multiplatform data classes. All of this can be executed as a script, while being strongly typed and compiled giving additional feedback to the autonomous chain-of-code style agent. This allows agents to define ad hoc data ingestion and retrieval schemas, while avoiding double-task inference challenge of encoding intents while comprehending own intents.