Topic
LangChain Memory & Conversational State
12 posts on langchain memory & conversational state — part of framework tutorials on the n4n AI blog.
Token-efficient conversation summarization in LangChain
Learn practical langchain token efficient summarization patterns for production conversational systems — memory types, custom strategies, and pitfalls to avoid.
Persisting LangChain memory to Postgres
A step-by-step guide to implementing LangChain memory persistence with Postgres, including schema design, custom memory classes, and production-ready patterns.
LangChain memory for multi-user chat applications
Build production-ready multi-user chat with LangChain memory — isolation strategies, storage backends, and pitfalls that bite at scale.
Debugging LangChain memory that forgets earlier context
Step-by-step debugging guide for LangChain memory that drops earlier conversation turns, with runnable code to verify fixes.
Migrating LangChain memory to the message history API
Step-by-step guide to migrating deprecated LangChain memory classes to the modern BaseChatMessageHistory API with runnable code and verification steps.
LangChain summary memory with n4n.ai for long chats
Implement LangChain summary memory with n4n.ai to handle long conversations without token overflow, including fallback routing and usage metering.
LangChain memory with Redis for multi-session chatbots
Build production-ready multi-session chatbots using LangChain memory with Redis — complete setup, code patterns, and verification steps.
LangChain ConversationBufferWindowMemory tutorial
A hands-on tutorial for LangChain ConversationBufferWindowMemory with runnable code, expected outputs, and production patterns for managing conversational context windows.
Entity memory in LangChain: tracking facts across turns
A practical guide to implementing LangChain entity memory for tracking facts across conversation turns, with code examples and production considerations.
LangChain RunnableWithMessageHistory tutorial
Hands-on langchain runnablewithmessagehistory tutorial: build stateful LCEL conversational chains with custom memory backends and OpenAI-compatible APIs.
LangChain memory types explained: buffer vs summary
A practical definition of LangChain memory types buffer vs summary: how buffer and summary memory work, code samples, and when to use each for engineers building LLM chat systems.
Add conversational memory to a LangChain chatbot
Step-by-step guide to add conversational memory to a LangChain chatbot using buffer and summary memory, with runnable Python code and verification tips.
More topics in framework tutorials
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- LangGraph Multi-Agent Workflows15
- LlamaIndex Query Engines for RAG15
- AutoGen Multi-Agent Conversations & Group Chat14
- Choosing an AI Framework by Use Case14
- CrewAI Agent Roles & Task Design14
- CrewAI Real-World Crew Examples14
- Haystack RAG Pipelines14
- LangChain Agents & Tool Calling14
- LangChain Multi-Model Fallback & Routing14
- LangChain.js for Node & TypeScript14