Best LLM frameworks in 2026
Libraries for building apps and agents on top of language models. 8 tools, ranked by live GitHub and npm numbers.
| Tool | Pricing | GitHub stars | npm a week | Last release |
|---|---|---|---|---|
| CrewAIMIT-licensed Python framework for multi-agent workflows, with a paid platform for deployment. | Open source | 60.1k | None | 3 days ago |
| LangGraphLow-level orchestration framework from LangChain for long-running, stateful agents. | Open source | 43.9k | None | 11 hours ago |
| DSPyPython framework from Stanford NLP for programming language model systems instead of writing prompts. | Open source | 38.8k | None | 2 weeks ago |
| MastraTypeScript framework for AI agents and workflows, Apache 2.0 licensed, with a managed platform. | Open source | 29.1k | 2.3M | 5 days ago |
| Vercel AI SDKFree open source TypeScript toolkit from Vercel for building AI apps and agents across many model providers. | Open source | 27.5k | 34M | 12 hours ago |
| HaystackOpen source Python framework from deepset for building LLM applications, RAG pipelines and agents. | Open source | 26.9k | None | 5 days ago |
LLM Frameworks comparisons
Alternatives
How to choose
LLM frameworks are libraries that sit between your code and a model. They handle the repeated work: calling different providers through one interface, chaining steps, retrieving your own data for the model to read, and running agents that call tools.
They save time when an app has several steps or needs to work across providers. For a single call to one model, the provider's own SDK is often enough.
What to compare
- Languages supported
- Model providers and vector stores it connects to
- Agent and tool-calling support
- Tracing, evaluation and hosted add-ons
- License and release activity
Questions
Do I need a framework to build with a language model?
No. For one prompt and one response, the provider's SDK is simpler. A framework helps once you chain several steps, retrieve your own data, run agents or want to switch providers.