𝗖𝗼𝗻𝘃𝗲𝗿𝘀𝗮𝘁𝗶𝗼𝗻 𝗠𝗲𝗺𝗼𝗿𝘆 𝗳𝗼𝗿 𝗟𝗮𝗻𝗴𝗖𝗵𝗮𝗶𝗻 𝗔𝗴𝗲𝗻𝘁𝘀
You can make LangChain agents smarter by adding memory. This allows them to remember facts from previous messages.
In this post, I turn a support agent into a multi-turn tool.
In turn one, the agent looks up a customer and an invoice. In turn two, the agent creates a ticket without asking for IDs again.
Three key concepts to know:
- Checkpointer: This acts as short-term memory. It saves messages and state after every step.
- thread_id: This is your conversation key. Use the same ID to continue a chat. Use a new ID to start a fresh session.
- Store: This is long-term memory for facts like user preferences.
How it works in a support flow:
- User asks to look up customer ID 1042 and invoice 8891. The agent finds the data.
- User says "Create the ticket." The agent remembers the IDs from the first step and completes the task.
For testing, use MemorySaver. It keeps state in your local process.
Important: MemorySaver clears when your code stops running. For production, use a database like Postgres or SQLite. The code remains the same because the thread_id API does not change.
Keep sessions separate by using different thread IDs for different users. This prevents one person's data from leaking into another person's chat.
Check the langchain-agent-memory-nodejs-demo folder for the full scripts.
Source: https://dev.to/zsevic/conversation-memory-for-langchain-agents-31pa
Optional learning community: https://t.me/GyaanSetuAi