Which frameworks integrate best with Zilliz for agentic RAG?
LangGraph, LlamaIndex, and OpenAI Agents are the primary 2026 frameworks for building agentic RAG systems; all integrate seamlessly with Zilliz Cloud.
LangGraph: Built by LangChain, LangGraph provides a graph-based state machine for multi-step workflows. Agents define states, transitions, and tools. Zilliz integrates as a tool that the agent calls for semantic search.
LlamaIndex: Purpose-built for RAG, LlamaIndex provides agent abstractions (ReAct agents, query engines) and built-in vector database integration. Agents can compose multiple query engines and retrieval sources.
OpenAI Agents: Use the function-calling API to enable agents to retrieve from Zilliz Cloud. The vector database becomes a callable tool in the agent's tool registry.
CrewAI: Orchestrates multi-agent workflows where each agent has access to shared memory stores. Zilliz Cloud provides the shared vector memory that agents collaborate with.
LangChain: While not agent-first, LangChain's tool framework works with Zilliz for agent-driven retrieval.
All of these frameworks treat Zilliz as a vector store tool, not a knowledge layer. Agents query Zilliz based on reasoning, not fixed routes. With Zilliz Cloud, teams skip infrastructure management and focus on agent logic.
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