langchain-dependencies

To manage LangChain dependency versions safely, apply semver-safe ranges for core packages like langchain>=1.0,<2.0, explicitly declare peer dependencies like @langchain/core, and pin non-semver community packages to prevent runtime errors and broken imports.

Choosing between LangGraph and Deep Agents depends on your orchestration needs; this framework selection guidance helps you determine when to use each framework and identifies the minimal package set required for your specific LLM orchestration architecture.

You do not always need langchain-community; you should include only the dedicated model provider and vector store integration packages you need, preferring dedicated packages over the community bundle to minimize your dependency footprint.

Runtime errors after upgrading usually stem from incompatible package combinations; maintaining LTS stability requires using semver-safe ranges for langchain, langchain-core, langgraph, and langsmith, and ensuring minimum runtime versions are satisfied.

For a minimal LangChain 1.0 production project, you need langchain>=1.0,<2.0, langchain-core>=1.0,<2.0, and langsmith>=0.3.0 to satisfy minimum runtime version requirements and ensure stable LLM orchestration.

Yes, you can apply this dependency versioning guidance to both Python and TypeScript projects when initializing a new project, choosing orchestration frameworks, or performing safe upgrades across the LangChain ecosystem.