langchain-dependencies

To manage LangChain dependencies effectively, install a centralized package matrix that prescribes core packages and minimum versions. This standardizes your environment, preventing version conflicts and upgrade pitfalls across the LangChain ecosystem.

LangGraph and Deep Agents can be used in the same Python environment by following specific framework guidance. This skill provides best-practice wiring to select and configure compatible versions for both frameworks without causing dependency collisions.

Minimum version requirements for LangSmith and LangChain are established through a recommended dependency matrix. This skill defines the exact baseline package versions needed to ensure ecosystem compatibility and maintain long-term project stability.

LangChain dependency guidance applies directly to both Python and TypeScript projects. It defines environment requirements, versioning strategies, and installation best practices tailored for your specific stack across the entire LangChain ecosystem.

LangChain environments break after upgrading due to undocumented version constraints and conflicting package dependencies. Applying a standardized dependency matrix with prescribed minimum versions ensures consistent, safe upgrades and prevents these environment conflicts.

The best way to set up a multi-repo LangChain project is to enforce consistent dependency management through documented environment constraints. This skill provides the wiring and versioning strategies to maintain stability across multiple repositories.