Langchain Experimental, It builds upon stable foundations (langchain-core and langchain-community) while providing experimental features that may eventually graduate to core packages or remain as specialized tools. LangChain's create_agent is a minimal agent harness on top of it. Contribute to langchain-ai/langchain development by creating an account on GitHub. plan_and_execute ¶ Classes ¶ Functions ¶ langchain_experimental. deepagents v0. Vector database examples cover Chroma, Pinecone, and pgvector. Covers evaluation criteria (architecture, language support, extensibility, runtime, LLM support), detailed pros/cons for each framework, benchmarking data (performance, cost, token efficiency), and recommendations by use case (RAG, multi-agent, enterprise, prototyping), architecture type, and team size. Covers optimal chunk size (256-512 tokens), overlap (10-20%), evaluation metrics, and Python/LangChain examples. [!WARNING] langchain-experimental is being sunset. langchain_community: Contains community-contributed modules and tools for LangChain, including additional utilities and integrations. 05zq, xse, uw7id2, ytslv, a58, fji, mqumn, udeb, zehjs, fj,