# Modbender Skill Library MCP Langchain > --- - Skill: `tomevault-io/modbender-skill-library-mcp-langchain` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/modbender-skill-library-mcp-langchain` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/modbender-skill-library-mcp-langchain/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/modbender-skill-library-mcp-langchain --- --- name: LangChain description: Avoid common LangChain mistakes — LCEL gotchas, memory persistence, RAG chunking, and output parser traps. metadata: {"clawdbot":{"emoji":"🦜","requires":{"bins":["python3"]},"os":["linux","darwin","win32"]}} --- ## LCEL Basics - `|` pipes output to next — `prompt | llm | parser` - `RunnablePassthrough()` forwards input unchanged — use in parallel branches - `RunnableParallel` runs branches concurrently — `{"a": chain1, "b": chain2}` - `.invoke()` for single, `.batch()` for multiple, `.stream()` for tokens - Input must match expected keys — `{"question": x}` not just `x` if prompt expects `{question}` ## Memory Gotchas - Memory doesn't auto-persist between sessions — save/load explicitly - `ConversationBufferMemory` grows unbounded — use `ConversationSummaryMemory` for long chats - Memory key must match prompt variable — `memory_key="chat_history"` needs `{chat_history}` in prompt - `return_messages=True` for chat models — `False` returns string for completion models ## RAG Chunking - Chunk size affects retrieval quality — too small loses context, too large dilutes relevance - Chunk overlap prevents cutting mid-sentence — 10-20% overlap typical - `RecursiveCharacterTextSplitter` preserves structure — splits on paragraphs, then sentences - Embedding dimension must match vector store — mixing models causes silent failures ## Output Parsers - `PydanticOutputParser` needs format instructions in prompt — call `.get_format_instructions()` - Parser failures aren't always loud — malformed JSON may partially parse - `OutputFixingParser` retries with LLM — wraps another parser, fixes errors - `with_structured_output()` on chat models — cleaner than manual parsing for supported models ## Retrieval - `similarity_search` returns documents — `.page_content` for text - `k` parameter controls results count — more isn't always better, noise increases - Metadata filtering before similarity — `filter={"source": "docs"}` in most vector stores - `max_marginal_relevance_search` for diversity — avoids redundant similar chunks ## Agents - Agents decide tool order dynamically — chains are fixed sequence - Tool descriptions matter — agent uses them to decide when to call - `handle_parsing_errors=True` — prevents crash on malformed agent output - Max iterations prevents infinite loops — `max_iterations=10` default may be too low ## Common Mistakes - Prompt template variables case-sensitive — `{Question}` ≠ `{question}` - Chat models need message format — `ChatPromptTemplate`, not `PromptTemplate` - Callbacks not propagating — pass `config={"callbacks": [...]}` through chain - Rate limits crash silently sometimes — wrap in retry logic - Token count exceeds context — use `trim_messages` or summarization for long histories --- > Source: [modbender/skill-library-mcp](https://github.com/modbender/skill-library-mcp) — distributed by [TomeVault](https://tomevault.io).