LlamaIndex
Provide an open-source Python/TypeScript framework for context-augmented LLM and agent applications combining RAG primitives (data connectors, indexes, query engines, retrievers, rerankers) with an event-driven Workflow runtime and a multi-agent AgentWorkflow orchestrator over FunctionAgent / ReActAgent / CodeActAgent prebuilts.
Description
LlamaIndex began as the leading data framework for retrieval-augmented LLM applications and has, since 2024, grown an explicit agent layer on top of that retrieval substrate. Today LlamaIndex Inc. ships LlamaIndex OSS (MIT, Python and TypeScript) as 'an open-source framework to build agentic applications' with Workflows as the underlying event-driven runtime and AgentWorkflow as a multi-agent orchestrator. The framework positions RAG as one tool an agent can call rather than the whole application: 'LlamaIndex provides a framework for building agents including the ability to use RAG pipelines as one of many tools to complete a task'.
Solution
Event-driven Workflow: steps decorated with @step are triggered by typed Events and emit further Events. On top, prebuilt agents FunctionAgent (native tool calling) and ReActAgent (prompted ReAct) plus the multi-agent AgentWorkflow orchestrator routing via can_handoff_to and a root_agent.
Primary use cases
- agentic RAG with multiple retrievers and rerankers
- event-driven Workflow orchestration
- multi-agent topologies via AgentWorkflow with handoff
- graph-RAG via PropertyGraphIndex
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