GitHub user Kryst4lDem0ni4s added a comment to the discussion: [Discussion] The selection of Agentic/Taskflow frame
@imbajin I would have to suggest either CrewAI or Agno. CrewAI may have a higher memory overhead compared to baseline systems. CrewAI's NLP pipeline with HugeGraph's domain-specific embeddings can improve dynamic intent recognition. Agno's parallel processing capabilities are beneficial for handling high-volume L1 requests efficiently. I'm researching this more in detail, especially looking at the project's upcoming goals. Simply put: CrewAI: Pros: Native support for mixed synchronous/asynchronous execution Prebuilt integrations with Prometheus monitoring Cons: 15-20% higher memory overhead compared to baseline Agno claims performance improvements up to 1000 times that of traditional frameworks but the integration may require custom adapters for HugeGraph's Gremlin/Cypher hybrid interface. This seems like a solid investment into the performance. Adding to the above, LlamaIndex provides a graph-aware retrieval system with a recursive mechanism for hierarchical caching AND It integrates seamlessly with tools like CrewAI to enhance search-based queries and agentic pipelines. Frameworks with active communities and regular updates, like CrewAI and LlamaIndex are practical for use, but if we're leaning more towards a DIY approach, with a focus on performance, Agno should be prioritized. Also to be noted, LlamaIndex MAY lack native support for HugeGraph's distributed computer module, need to look further into it. CrewAI seems like the perfect option, memory efficiency aside. Memory efficiency considered, Agno must be looked into. How about hybrid approaches? GitHub link: https://github.com/apache/incubator-hugegraph-ai/discussions/203#discussioncomment-12666603 ---- This is an automatically sent email for dev@hugegraph.apache.org. To unsubscribe, please send an email to: dev-unsubscr...@hugegraph.apache.org
