LangChain & LangGraph Tutorial: In-Depth Chat Memory & Prompts

Conversational agents that not only react but also remember, adjust, and develop with your users are essential in today's AI environment. This guide goes into great detail about the significance of LangChain and LangGraph, as well as important ideas like conversation buffer memory and prompt templates, how to use them step-by-step, where to use them in your own projects, and who you can work with to make your ideas a reality. You will have a clear, practical route to creating reliable, context-aware chat applications by the end.

Why LangChain & LangGraph?

Developing a stateful, multi-turn chatbot frequently results in tangled code because of brittle prompt logic, ad hoc memory systems, and manual API calls. These issues are abstracted by LangChain using modular "chains," in which each link manages a different function, such as memory storage, tool usage, prompt rendering, or API invocation. By adding a robust graph-based memory layer, LangGraph transforms linear chat logs into intricate networks of entities, relationships, and messages. When combined, they free you from boilerplate plumbing so you can concentrate on creating outstanding user experiences.

Benefits of LangChain & LangGraph

• Clarity & Maintainability: Prebuilt classes read like a script, not a maze of API calls

• Flexibility & Extensibility: Swap memory backends or add new tools with minimal code changes

• Scalability & Deployment: Built-in support for Azure OpenAI and Streamlit lets you launch quickly

• Community Support: Active Discord and GitHub communities for rapid Q&A and shared examples

What You’ll Learn in This Guide

• Basic Agent Anatomy – How chains, prompts, and LLM wrappers fit together

• Prompt Templates – Craft dynamic, context-rich prompts to guide your AI’s behavior

• Conversation Buffer Memory – Capture system, user, and AI messages for coherent dialogue

• LangGraph Integration – Model memory as a graph for smarter retrieval and analysis

• Streamlit Chat Demo – Build a full-featured chat UI that persists memory across sessions.

Overview of LangChain Agents

Anatomy of a Basic Agent

An agent is essentially a pipeline of modular components:

from langchain import OpenAI, LLMChain, PromptTemplate
prompt = PromptTemplate(

    input_variables=["user_input"],

    template="You are a helpful assistant. User says: {user_input}"

)

llm = OpenAI(temperature=0.7)

agent = LLMChain(llm=llm, prompt=prompt)

• PromptTemplate: Defines the conversation script with placeholders

• OpenAI Wrapper: Handles API calls, temperature, token limits

• LLMChain: Binds template and model into a callable agent

Chains, Tools & Callbacks

• Chains: Sequence operations (e.g., retrieval → summarization → answer)

• Tools: Specialized functions (search, calculator) that agents can invoke

• Callbacks: Hooks for logging, visualization, or side-effects on each call

Deep Dive into Prompt Templates

What Is a Prompt Template?

A prompt template is your agent’s “script”—a text blueprint with dynamic slots. Well-crafted templates steer the LLM toward consistent, on-brand responses.

Components of a Prompt Template

Template Variables: -

Placeholders like {user_input}, {history}, or {context} let you inject real-time data.

Example:

template = "Context:\n{history}\n\nQuestion:\n{user_input}"

Crafting Advanced Templates

• Few-Shot Examples: Include 2–3 sample Q&A pairs to set expectations

• Conditional Logic: Use Jinja-style loops or conditionals to handle lists

• Validation: Leverage Pydantic or built-in checks to enforce variable types

What is Conversation Buffer Memory

Role of Conversation Buffer Memory

This component tracks the sequence of messages—system → user → AI → user → AI…ensuring each new call has full context.

System, User & AI Messages

System Messages

Set behavior or persona (e.g., “You are a witty, concise assistant.”).

User Messages

Every user input is appended to the buffer:

memory.save_context({"user": user_input}, {})

AI Responses

Generate replies using the full history:

response = agent.predict(input=user_input, memory=memory)

memory.save_context({}, {"ai": response})

Visit the GitHub Repository for full implementation of the code

Best Practices & Optimization Tips for LangChain and LangGraph

Secure API Keys

• Store in environment variables or secret managers—never in source control.

Efficient Memory Management

• Prune old messages beyond a set limit

• Summarize long histories into concise embeds

• Filter via LangGraph to load only relevant subgraphs

Prompt Engineering Strategies

• Start with System Messages: Frame the assistant’s role first

• Use Examples: Anchor expected style and format

• Analyze Logs: Refine prompts based on real user interactions

Through the use of LangGraph's robust node-and-edge model and LangChain's modular memory components, you can enhance your agents with capabilities such as on-the-fly context summarization, automated entity resolution across turns, and dynamic conversation branching based on detected intents. Your AI interactions will become smarter and more tailored thanks to this synergy, which allows you to surface only the most pertinent historical context for each user query, allows for fine-grained control over dialogue flow, and supports advanced use cases like changing a user's profile over time or triggering tool invocations when particular conversation patterns emerge.