Build AI Agent Memory with Graphiti Knowledge Graphs
Plus stream million-row CSVs with Polars
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📅 Today’s Picks
Build AI Agent Memory with Graphiti Knowledge Graphs
Problem
Traditional RAG pipelines rely on batch processing and static document summaries. When data changes, you re-embed, re-index, and wait.
That delay means your agent is always working with stale information, unable to track how facts evolve over time.
Solution
Graphiti is an open-source Python framework that builds knowledge graphs with real-time, incremental updates. This lets you add new information at any time without reprocessing your entire dataset.
Key features:
Track when facts happened and when they were recorded, so you always know what’s current
Search by meaning, keywords, or relationships in one query
Get the most relevant results for a specific person, company, or entity
Works with Neo4j, FalkorDB, and Kuzu as the graph backend
Polars sink_csv: Stream Million-Row Exports Without Memory Spikes
Problem
Writing large DataFrames to CSV is memory-intensive because the entire dataset is serialized in memory before being written to disk.
Solution
Polars’ streaming CSV sink avoids this by writing data in chunks rather than all at once.
Key benefits:
Eliminate out-of-memory errors on large exports
Write multi-million row DataFrames with minimal RAM
Support for cloud storage destinations (S3, GCS, Azure)
Switch from write_csv to sink_csv on a lazy frame to enable streaming.
📖 View Full Article | ⭐ View GitHub
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