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 (9)
- production
How to Reduce LangChain Token Costs in Agents and RAG
Cut LangChain token spend with prompt caching, bounded history, cleared tool outputs, fewer tool schemas, call limits and model routing.
- how-to
How to Stream LangChain Responses: Models, Agents, APIs
Stream LangChain output with model.stream(), agent stream_mode messages, and astream_events, then ship tokens over SSE without proxy buffering.
- retrieval
LangChain Chunk Size: Configure Splitters for RAG
Configure LangChain chunk_size and chunk_overlap, choose character or token units, preserve source metadata, and compare splitter settings for RAG.
- agents
LangChain Memory Types: Legacy Classes and Replacements
This guide compares legacy LangChain memory classes with 1.x checkpointers, stores, trimming, and summarization for short- and long-term memory.
- integrations
LangChain with Ollama: Local Setup, Tools, and RAG
This guide explains how LangChain connects to Ollama for local chat, streaming, tool calling, structured output, embeddings, and RAG.
- 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: Agents, State and Control Flow
Compare LangChain vs LangGraph for agent loops, state, checkpoints and human approval, with guidance on when to use create_agent or a custom graph.
- 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.