using-langchain

LangChain 1.x patterns for chains, tools, memory, and structured outputs

Build production LangChain applications using LCEL, tools, memory, and structured outputs.

Process

1. Initialize LLM – Use aisuite for provider-agnostic access or LangChain native integrations (e.g. ChatGoogleGenerativeAI).
2. Create Chains – Compose with LCEL pipe syntax: prompt | llm | parser. Use ChatPromptTemplate for templated prompts.
3. Structured Outputs – Define Pydantic models; use PydanticOutputParser for type-safe responses.
4. Tool Calling – Decorate functions with @tool, bind with llm.bind_tools(), invoke and handle tool calls.
5. Memory – Use RunnableWithMessageHistory with InMemoryChatMessageHistory for session-scoped context.
6. Document Loaders – Use PyPDFLoader, WebBaseLoader, CSVLoader from langchain_community.

LCEL Patterns

| Pattern | Example | ||| | Sequential | chain1 \| chain2 \| chain3 | | Parallel | RunnableParallel(a=chain1, b=chain2) | | Conditional | RunnableBranch((condition, chain1), chain2) | | Fallback | chain.with_fallbacks([backup]) | | Retry | chain.with_retry(stop_after_attempt=3) |

Best Practices

• Use LCEL pipe syntax for chain composition
• Always use async methods (ainvoke, astream) for I/O
• Define tools with proper docstrings for LLM understanding
• Use Pydantic for structured outputs
• Enable tracing with LangSmith
• Handle errors with fallbacks

Anti-Patterns

| Anti-Pattern | Fix | |--|--| | Sync in async context | Use ainvoke not invoke | | No error handling | Add .with_fallbacks() | | Hardcoded prompts | Use ChatPromptTemplate | | No type hints | Use Pydantic models |

MCP Integration (New)

Use the docs-langchain MCP server to search for the latest patterns and API references directly:

# Search for specific patterns
response = await client.chat.completions.create(
    messages=[{"role": "user", "content": "How do I use RunnableWithMessageHistory?"}],
    tools=[{
        "type": "mcp",
        "name": "docs-langchain",
        "command": "npx", # Managed by MCP config
        "args": []
    }]
)

Bundled Resources

• QUICKSTART.md – 5-minute getting started guide
• scripts/verify.py – Validate project follows LangChain patterns (--project-dir)
• examples/basic_chain/ – Simple LCEL chain with structured output

When to Use

This skill should be used when strict adherence to the defined process is required.

Prerequisites

• Basic understanding of the agent factory context.
• Access to the necessary tools and resources.