cursor-langchain

LangChain Cursor Rules

You are an expert in LangChain development. Follow these rules:

Chains (LCEL)

• Use LangChain Expression Language (LCEL) with pipe operator: prompt | llm | parser
• Use RunnablePassthrough, RunnableParallel, RunnableLambda for chain composition
• Always end chains with an output parser: StrOutputParser, JsonOutputParser, PydanticOutputParser
• Use .with_config(run_name="...") for observability in LangSmith traces
• Use .batch() and .abatch() for parallel execution over multiple inputs

Prompts

• Use ChatPromptTemplate.from_messages() for chat models — never raw string formatting
• Use MessagesPlaceholder for dynamic message insertion (history, agent scratchpad)
• Keep system prompts in separate files or constants — not inline in chain definitions
• Use .partial() to pre-fill template variables at chain construction time

Agents

• Use create_tool_calling_agent() with tool-calling models — not legacy AgentExecutor patterns
• Define tools with @tool decorator — include docstrings (the LLM reads them)
• Set max_iterations on AgentExecutor to prevent infinite loops (default is too high)
• Use return_intermediate_steps=True for debugging and logging agent reasoning

Memory & History

• Use RunnableWithMessageHistory for conversation state — not ConversationBufferMemory (legacy)
• Store history in persistent backends (Redis, PostgreSQL) — not in-memory for production
• Trim history with trim_messages() to control context window usage
• Use ConversationSummaryMemory only when context window is a hard constraint

Retrieval (RAG)

• Use create_retrieval_chain() or LCEL with retriever | format_docs | prompt | llm
• Set search_kwargs={"k": 4} explicitly on retrievers — don't rely on defaults
• Use RecursiveCharacterTextSplitter with appropriate chunk_size and chunk_overlap
• Add metadata to documents at indexing time for filtered retrieval

Tools & Output

• Tools must have clear, concise descriptions — the model selects tools based on descriptions
• Use PydanticOutputParser with output_fixing_parser for structured LLM output
• Handle LLM errors with fallbacks: chain.with_fallbacks([fallback_chain])
• Use callbacks for streaming: chain.stream() or chain.astream() for real-time output