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Active Benchmark // PROD-v2.4Latency: 90s

langgraph_orchestration.py

Async: StateGraph

workflow = StateGraph(LoanIntakeState)workflow.add_node("triage_agent", node_seq_extractor)workflow.add_node("hybrid_rag", node_pinecone_bm25)workflow.add_node("deterministic_audit", audit_guard)// Turnaround reduced from 4.2h to 90s

Retrieval Confidence: 0.998Pinecone Hybrid v2

Multi-Agent SwarmsFastAPI 0.115 + CUDA

AI & MULTI-AGENT SYSTEMS12 min read · Published Oct 2024

Architecting Deterministic Multi-Agent Swarms with LangGraph and Hybrid Vector RAG in Production

Why single-prompt LLM wrappers fail at scale. How we eliminated hallucination cascades and reduced loan intake verification turnaround from 4.2 hours to 90 seconds using DAG state machines and Pinecone hybrid embeddings.

#LangGraph#HybridRAG#PythonFastAPI#EnterpriseAI#Pinecone

Rafiqul Karim

CTO & Co-Founder, GrowBig IT

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#VectorDB#CohereRerank#PrivateVPC

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#Nextjs15#Supabase#VibeCoding

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#WhatsAppAPI#n8n#MetaCloudAPI

Elena Rostova

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