Langchain

LangChain is a community skill for building applications powered by language models using the LangChain framework, covering chain composition, prompt management, memory systems, retrieval integration, and agent orchestration.

What Is This?

Overview

LangChain provides patterns for composing language model calls into structured application workflows. It covers chain construction linking prompts to model calls and output parsers, memory components that maintain conversation state, retrieval chains that augment prompts with external knowledge, tool-using agents that select actions dynamically, and output parsing that converts model text into structured data. The skill enables developers to build complex LLM applications from reusable components.

Who Should Use This

This skill serves developers building chatbots, question-answering systems, and AI assistants that require multi-step reasoning, teams integrating language models into existing applications with structured input and output requirements, and engineers building agent systems that combine model reasoning with tool execution.

Why Use It?

Problems It Solves

Raw API calls to language models require manual prompt formatting, response parsing, and error handling for every interaction. Multi-step workflows that chain model outputs into subsequent prompts are tedious to build from scratch. Maintaining conversation history across turns requires custom state management. Connecting models to external data sources and tools needs orchestration logic that grows complex quickly.

Core Highlights

Chain abstraction composes prompt templates, model calls, and output parsers into reusable pipelines. Memory components store and retrieve conversation history with configurable retention strategies. Retrieval integration connects vector stores and search engines to augment prompts with relevant context. Agent framework enables models to select and execute tools based on reasoning about the current task.

How to Use It?

Basic Usage

from dataclasses import dataclass, field
from typing import Any, Callable

@dataclass
class PromptTemplate:
    template: str
    variables: list[str]

    def format(self, **kwargs) -> str:
        result = self.template
        for var in self.variables:
            result = result.replace(f"{{{var}}}", str(kwargs.get(var, "")))
        return result

@dataclass
class Chain:
    prompt: PromptTemplate
    llm_call: Callable
    output_parser: Callable = None

    def run(self, **kwargs) -> Any:
        formatted = self.prompt.format(**kwargs)
        response = self.llm_call(formatted)
        if self.output_parser:
            return self.output_parser(response)
        return response

class SequentialChain:
    def __init__(self, chains: list[Chain],
                 input_keys: list[str],
                 output_keys: list[str]):
        self.chains = chains
        self.input_keys = input_keys
        self.output_keys = output_keys

    def run(self, **kwargs) -> dict:
        current = kwargs
        for i, chain in enumerate(self.chains):
            result = chain.run(**current)
            current[self.output_keys[i]] = result
        return current

Real-World Examples

from dataclasses import dataclass, field

@dataclass
class ConversationMemory:
    messages: list[dict] = field(default_factory=list)
    max_messages: int = 20

    def add(self, role: str, content: str):
        self.messages.append({"role": role, "content": content})
        if len(self.messages) > self.max_messages:
            self.messages = self.messages[-self.max_messages:]

    def get_context(self) -> str:
        return "\n".join(
            f"{m['role']}: {m['content']}" for m in self.messages
        )

@dataclass
class RetrievalChain:
    retriever: Any
    chain: Chain
    memory: ConversationMemory = None

    def __post_init__(self):
        if self.memory is None:
            self.memory = ConversationMemory()

    def query(self, question: str) -> str:
        context = self.retriever(question)
        history = self.memory.get_context()
        result = self.chain.run(
            question=question, context=context, history=history
        )
        self.memory.add("user", question)
        self.memory.add("assistant", result)
        return result

Advanced Tips

Use output parsers with retry logic that reprompts the model when the initial response fails to match the expected format. Implement streaming for chains that involve long model responses to provide progressive feedback to users. Add callbacks at each chain step for logging and debugging complex multi-step workflows.

When to Use It?

Use Cases

Build a customer support chatbot with memory that retrieves answers from a product knowledge base. Create a document analysis pipeline that summarizes, extracts entities, and generates reports in sequence. Implement an agent that uses search, calculation, and database tools to answer complex multi-step questions.

Related Topics

LLM application frameworks, prompt engineering patterns, vector store integration, agent architectures, and conversational AI memory systems.

Important Notes

Requirements

The langchain Python package with appropriate model provider integrations installed. API keys for the target language model service. Vector store setup for retrieval-augmented applications.

Usage Recommendations

Do: start with simple chains and add complexity incrementally as requirements become clearer. Use structured output parsers to enforce response formats for downstream processing. Test each chain component independently before composing them into sequences.

Don't: nest chains deeply without intermediate validation that catches errors early. Store sensitive data in memory components without considering privacy and retention policies. Ignore token usage tracking when chaining multiple model calls that compound costs.

Limitations

Framework abstractions add overhead and complexity that may be unnecessary for simple single-call applications. Debugging multi-step chains requires tracing through abstraction layers to find where errors originate. Rapid framework updates can introduce breaking changes that require frequent dependency management.