LangGraph: Persistence

LangChain · Intermediate ·🤖 AI Agents & Automation ·2y ago

Key Takeaways

LangGraph agents with persistence using LangChain

Original Description

This video goes over how to add persistence to LangGraph agents. This allows for "memory" in your LangGraph agent applications. Notebook: https://github.com/langchain-ai/langgraph/blob/main/examples/persistence.ipynb
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1 Chat With Your Documents Using LangChain + JavaScript
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2 LangChain SQL Webinar
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3 LangChain "OpenAI functions" Webinar
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4 LangSmith Launch
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5 LangChain x Pinecone: Supercharging Llama-2 with RAG
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6 LangChain Expression Language
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7 Building LLM applications with LangChain with Lance
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8 Benchmarking Question/Answering Over CSV Data
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9 LangChain "RAG Evaluation" Webinar
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10 Fine-tuning in Your Voice Webinar
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11 Tabular Data Retrieval
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12 Building an LLM Application with Audio by AssemblyAI
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13 Superagent Deepdive Webinar
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14 Lessons from Deploying LLMs with LangSmith
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15 Shortwave Assistant Deepdive Webinar
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16 Cognitive Architectures for Language Agents
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17 Effectively Building with LLMs in the Browser with Jacob
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18 Data Privacy for LLMs
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19 "Theory of Mind" Webinar with Plastic Labs
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20 LangChain Templates
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21 Using Natural Language to Query Postgres with Jacob
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22 Building a Research Assistant from Scratch
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23 Benchmarking RAG over LangChain Docs
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24 Skeleton-of-Thought: Building a New Template from Scratch
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25 Benchmarking Methods for Semi-Structured RAG
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26 LangSmith Highlights: Getting Started
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27 LangSmith Highlights: Debugging
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28 LangSmith Highlights: Datasets
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29 LangSmith Highlights: Evaluation
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30 LangSmith Highlights: Human Annotation
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31 LangSmith Highlights: Monitoring
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32 LangSmith Highlights: Hub
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33 SQL Research Assistant
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34 Getting Started with Multi-Modal LLMs
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35 Build a Full Stack RAG App With TypeScript
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36 Auto-Prompt Builder (with Hosted LangServe)
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37 LangChain v0.1.0 Launch: Introduction
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38 LangChain v0.1.0 Launch: Observability
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39 LangChain v0.1.0 Launch: Integrations
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40 LangChain v0.1.0 Launch: Composability
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41 LangChain v0.1.0 Launch: Streaming
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42 LangChain v0.1.0 Launch: Output Parsing
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43 LangChain v0.1.0 Launch: Retrieval
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44 LangChain v0.1.0 Launch: Agents
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45 Build and Deploy a RAG app with Pinecone Serverless
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46 Hosted LangServe + LangChain Templates
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47 LangGraph: Intro
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48 LangGraph: Agent Executor
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49 LangGraph: Chat Agent Executor
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50 LangGraph: Human-in-the-Loop
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51 LangGraph: Dynamically Returning a Tool Output Directly
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52 LangGraph: Respond in a Specific Format
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53 LangGraph: Managing Agent Steps
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54 LangGraph: Force-Calling a Tool
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55 LangGraph: Multi-Agent Workflows
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56 Streaming Events: Introducing a new `stream_events` method
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57 Building a web RAG chatbot: using LangChain, Exa (prev. Metaphor), LangSmith, and Hosted Langserve
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58 OpenGPTs
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59 Open Source RAG with Nomic's New Embedding Model (and ChromaDB and Ollama)
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LangGraph: Persistence
LangGraph: Persistence
LangChain

This video teaches how to add persistence to LangGraph agents using LangChain, allowing for memory in agent applications. By following the lesson, viewers can build persistent LangGraph agents and add memory to their applications.

Key Takeaways
  1. Import necessary libraries
  2. Initialize LangGraph agent
  3. Add persistence to the agent
  4. Test the agent's memory
  5. Deploy the agent application
💡 Persistence is crucial for LangGraph agents to retain information and make informed decisions.

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