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Generative AI with LangChain

You're reading from   Generative AI with LangChain Build production-ready LLM applications and advanced agents using Python, LangChain, and LangGraph

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Product type Paperback
Published in May 2025
Publisher Packt
ISBN-13 9781837022014
Length 480 pages
Edition 2nd Edition
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Authors (2):
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Ben Auffarth Ben Auffarth
Author Profile Icon Ben Auffarth
Ben Auffarth
Leonid Kuligin Leonid Kuligin
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Leonid Kuligin
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Table of Contents (14) Chapters Close

Preface 1. The Rise of Generative AI: From Language Models to Agents 2. First Steps with LangChain FREE CHAPTER 3. Building Workflows with LangGraph 4. Building Intelligent RAG Systems 5. Building Intelligent Agents 6. Advanced Applications and Multi-Agent Systems 7. Software Development and Data Analysis Agents 8. Evaluation and Testing 9. Production-Ready LLM Deployment and Observability 10. The Future of Generative Models: Beyond Scaling 11. Other Books You May Enjoy
12. Index Appendix

Agent memory

We discussed memory mechanisms in Chapter 3. To recap, LangGraph has the notion of short-term memory via the Checkpointer mechanism, which saves checkpoints to persistent storage. This is the so-called per-thread persistence (remember, we discussed earlier in this chapter that the notion of a thread in LangGraph is similar to a conversation). In other words, the agent remembers our interactions within a given session, but it starts from scratch each time.

As you can imagine, for complex agents, this memory mechanism might be inefficient for two reasons. First, you might lose important information about the user. Second, during the exploration phase when looking for a solution, an agent might learn something important about the environment that it forgets each time – and it doesn’t look efficient. That’s why there’s the concept of long-term memory, which helps an agent to accumulate knowledge and gain from historical experiences, and enables...

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