Orchestration and Workflows

How each AI framework coordinates multi-step or multi-agent work.

Vercel AI SDK

Orchestration is comparatively lightweight, built around stopWhen stopping conditions and the newer Agent abstraction rather than a dedicated workflow or graph engine.

TanStack AI

Offers composable “strategy functions” for controlling an agent loop, combining conditions like iteration limits and token budgets, rather than a full graph-based orchestration engine.

LangChain

Provides chains and agent executors for orchestration, though complex, stateful multi-step workflows are increasingly delegated to its sibling project, LangGraph.

LangGraph

Purpose-built for orchestration — workflows are modeled as an explicit graph of nodes and edges with persisted state, supporting cycles, branching, and durable checkpoints for production use.

LlamaIndex

Offers “workflows” as an explicit orchestration primitive for chaining retrieval and generation steps, alongside simpler query-engine abstractions for straightforward RAG use cases.

Mastra

Ships a dedicated workflow engine designed for reliability — if a step fails, the workflow resumes from that step rather than re-running the whole thing and re-spending LLM tokens.

CrewAI

Orchestrates multiple agents as a “crew” executing tasks sequentially or hierarchically, with a process manager coordinating handoffs between agents.

Semantic Kernel (Microsoft Agent Framework)

Following its merger with AutoGen, provides unified orchestration for both single-agent workflows and multi-agent conversations and handoffs within one framework.

Haystack

Orchestrates work as directed pipelines of components (retriever, ranker, generator, and so on), a narrower, more linear model than the general-purpose agent graphs used elsewhere in this category.