Editorial desk
LangChainGuide Editorial
LangChainGuide Editorial is the publishing identity for LangChainGuide. It is a desk, not a person: no named author, no biography, no professional certifications.
Articles published under this byline are researched from primary sources — vendor and project documentation, published standards and specifications, research papers, and measurements published by whoever took them — drafted with AI assistance, and edited against those cited sources before publication. Nothing here is based on first-hand testing in a private lab, and any figure that appears is attributed to the source it came from.
Corrections go to [email protected]. More detail is on the about page and the editorial disclosure.
Posts (5)
- Integrations
How to Use LangChain with Ollama: Local Chat, Tools, Structured Output and Embeddings
Wire ChatOllama and OllamaEmbeddings to a local Ollama server: install, the context window and keep_alive settings that decide whether it holds up, bind_tools, with_structured_output, and the failure modes to expect.
- Troubleshooting
LangChain Agent Errors: Loops, Tools, Parsing
Why LangChain agents loop forever, skip tools, pass bad arguments or fail to parse output, and the configuration changes that fix each symptom.
- Comparisons
LangChain vs LangGraph vs LlamaIndex Compared
LangChain, LangGraph and LlamaIndex solve different problems. A side-by-side comparison of scope, state model, retrieval depth and which one to pick.
- Retrieval
LangChain RAG Pipeline: Setup to First Answer
How a LangChain RAG pipeline fits together: loading, chunking, embeddings, vector storage and retrieval, plus the settings that decide answer quality.
- Fundamentals
LangChain Building Blocks: Chains, Tools and Agent Control
What LangChain actually abstracts, when an agent loop is the wrong choice, and how to keep retrieval, memory and token cost under control in production.