Jupyter Notebook multi-agent-system Projects
-
agents-towards-production
End-to-end, code-first tutorials for building production-grade GenAI agents. From prototype to enterprise deployment.
Project mention: Show HN: GibRAM an in-memory ephemeral GraphRAG runtime for retrieval | news.ycombinator.com | 2026-01-17GraphRAG's approach certainly makes sense for regulation-heavy documents, where interconnected references can be critical for retrieval quality. The challenge you mentioned about separate systems for graph storage and vector indexing resonates with what we've seen in the field. In practice, this separation often adds unnecessary complexity for ephemeral analysis tasks. One thing we've explored is using a dual-memory architecture, which might be relevant here even in an in-memory context. It allows you to effectively manage recent context and longer-term semantic connections without the overhead of persistent storage. If you're interested in seeing how this approach could be integrated into production systems, I wrote up some patterns that might be useful: https://github.com/NirDiamant/agents-towards-production.
-
AppSignal
AppSignal knows why the f*#k it crashed. Stop vibe-debugging. Every exception, every backtrace, grouped so you see patterns, not noise.
-
Jupyter Notebook multi-agent-systems discussion
Index
| # | Project | Stars |
|---|---|---|
| 1 | agents-towards-production | 21,386 |
| 2 | phi | 278 |
Sponsored