DSPy

The default open-source framework for composing LLM apps.

Python + TypeScript framework for chaining prompts, tools, retrievers, and memory into LLM applications. Ubiquitous in the ecosystem; pairs with LangGraph for agent orchestration and LangSmith for tracing.

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.

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.

Type-safe Python agent framework, the Pydantic way.

An open-source Python framework for building production-grade agents with validated, structured outputs instead of raw-string parsing. Model-agnostic across OpenAI, Anthropic, Gemini and many more, with composable tools, durable execution, MCP support, and built-in observability via Pydantic Logfire. MIT-licensed from the team behind Pydantic.