GitHub user Kryst4lDem0ni4s added a comment to the discussion: [Discussion] The
selection of Agentic/Taskflow frame
@imbajin , I agree with @Aryankb's understanding but as for the workflow
priority, here are my two cents:
```mermaid
graph TD
A[User Query] --> B(Agno L1 Processor)
B -->|Simple Lookup| C[HugeGraph Cache]
B -->|Complex Query| D{CrewAI Orchestrator}
D -->|Multi-Hop| E[LlamaIndex Retriever]
D -->|Computation| F[HugeGraph-Computer]
F -->G
E --> G[Result Aggregator]
G --> H[Pydantic Validator]
H --> I[Output Formatter]
```
Here, we'll be considering the top four candidate frameworks (CrewAI, Agno,
LlamaIndex, Pydantic-AI) against HugeGraph's requirements for implementing an
agentic GraphRAG system.
Instead of taking one out of the bunch, I'll reiterate my suggestion for the
hybrid approach.
Deploy Agno for L1 queries
Implement CrewAI's dynamic classifier with HugeGraph embeddings
Develop hybrid caching layer (RocksDB + Agno shared memory)
Build Pydantic validation middleware
Introduce LlamaIndex recursive retrieval
CrewAI's Performance Profile (I asked chatgpt for an analysis)
```
Throughput: 8,500 QPS (L2 queries)
Memory: 1.8GB per orchestration node
Latency: 45ms P99 for complex workflows
Key Advantages
Native integration with HugeGraph's RocksDB-based embeddings
Prebuilt Prometheus metrics exporter for OLAP/OLTP monitoring
```
So summing it up here is the proposed architecture, kept simple:
> Base Layer (Agno)
> Handle high-frequency L1 queries through optimized parallel execution
> Implement Gremlin-Cypher transpiler for hybrid query support
> Orchestration Layer (CrewAI)
> Manage complex workflows using dynamic intent classification
> Integrate with HugeGraph's priority queue system
> Validation Layer (Pydantic-AI)
> Enforce schema consistency across all graph operations
> Provide developer-friendly type hints
> Retrieval Enhancement (LlamaIndex)
> Implement recursive retrieval with tiered caching
> Integrate with HugeGraph's OLAP engine
My rationale and research summarized:
Agno delivers performance gains for core operations while maintaining lower
memory usage
CrewAI's workflow engine reduces development time for complex agent
interactions compared to manual implementations
Hybrid model achieves a much higher fault recovery through layered fallback
mechanisms
This proposed architecture is based off of what I saw on the apache's jira,
where the required architecture was provided for the upcoming months of
development.
I also emailed you additional insights for the architecture, please do check (
@imbajin )
GitHub link:
https://github.com/apache/incubator-hugegraph-ai/discussions/203#discussioncomment-12666607
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