Pydantic AI

Graph-based agent orchestration. Stateful loops with checkpoints.

LangChain's agent layer. Model agents as nodes in a state graph with persistent checkpoints, human-in-the-loop steps, and durable execution. Strong when you need a long-running, debuggable agent rather than a one-shot chain.

Multi-agent framework with explicit roles and tasks.

Python framework for orchestrating crews of specialised agents — researcher, writer, reviewer — coordinated through shared context. Opinionated about roles, sequencing, and delegation; good fit for content-and-research pipelines.

The data framework for LLM apps — RAG, agents, and document workflows.

An open-source framework (Python + TypeScript) for connecting LLMs to your data — ingestion, indexing, retrieval, and agentic document workflows. Pairs with the managed LlamaCloud (LlamaParse) for production parsing and extraction. The most-used RAG framework after LangChain.

TypeScript framework for building AI agents and workflows.

An open-source TypeScript stack for AI applications: agents, a graph-based workflow engine (.then/.branch/.parallel), memory, tools, and built-in observability behind one API. Run it self-hosted under Apache 2.0, or deploy to Mastra Cloud with a free Starter tier and paid Teams/Enterprise plans.