De-list Deprecated Tutorials (#407)

README.md

@@ -10,8 +10,7 @@
 ## Overview
 
 [LangGraph](https://langchain-ai.github.io/langgraph/) is a library for building stateful, multi-actor applications with LLMs.
-Inspired by [Pregel](https://research.google/pubs/pub37252/) and [Apache Beam](https://beam.apache.org/), LangGraph lets you coordinate and checkpoint multiple chains (or actors) across cyclic computational steps using regular python functions (or [JS]()).
-The current public interface draws inspiration from [NetworkX](https://networkx.org/documentation/latest/).
+Inspired by [Pregel](https://research.google/pubs/pub37252/) and [Apache Beam](https://beam.apache.org/), LangGraph lets you coordinate and checkpoint multiple chains (or actors) across cyclic computational steps using regular python functions (or [JS](https://github.com/langchain-ai/langgraphjs)). The public interface draws inspiration from [NetworkX](https://networkx.org/documentation/latest/).
 
 The main use is for adding **cycles** and **persistance** to your LLM application. If you only need quick Directed Acyclic Graphs (DAGs), you can already accomplish this using [LangChain Expression Language](https://python.langchain.com/docs/expression_language/).
 

docs/docs/reference/checkpoints.md

@@ -1,6 +1,6 @@
 # Checkpoints
 
-You can [compile](https://langchain-ai.github.io/langgraph/reference/graphs/#langgraph.graph.MessageGraph.compile) any LangGraph workflow with a [CheckPointer]() to give your agent "memory" by persisting its state. This permits things like:
+You can [compile](https://langchain-ai.github.io/langgraph/reference/graphs/#langgraph.graph.MessageGraph.compile) any LangGraph workflow with a [CheckPointer](https://langchain-ai.github.io/langgraph/reference/checkpoints/#checkpoint) to give your agent "memory" by persisting its state. This permits things like:
 
 - Remembering things across multiple interactions
 - Interrupting to wait for user input

docs/docs/tutorials/index.md

@@ -8,22 +8,6 @@ Learn the basics of LangGraph through the onboarding tutorials.
 
 - [Introduction to LangGraph](introduction.ipynb)
 
-
-## AgentExecutor
-
-Learn to build a simple agent in LangGraph.
-
-- [Base](agent_executor/base.ipynb): Learn to build a LangGraph agent "from scratch"
-- [High-Level](agent_executor/high-level.ipynb): Learn to use the `create_agent_executor`
-
-## Chat Agent Executor
-
-Learn to build a simple chat agent executor, which is a basic graph with an agentic loop that also supports dialog with a user.
-
-- [Base](chat_agent_executor_with_function_calling/base.ipynb): Build a chat agent executor with function calling
-- [High-Level](chat_agent_executor_with_function_calling/high-level.ipynb): Using the high-level chat agent executor API
-- [High-Level Tools](chat_agent_executor_with_function_calling/high-level-tools.ipynb): Integrating tools into the high-level chat agent executor
-
 ## Use cases
 
 Learn from example implementations of graphs designed for specific scenarios and that implement common design patterns.
@@ -33,7 +17,6 @@ Learn from example implementations of graphs designed for specific scenarios and
 - [Customer Support](customer-support/customer-support.ipynb): Build a customer support chatbot to manage flights, hotel reservations, car rentals, and other tasks
 - [Info Gathering](chatbots/information-gather-prompting.ipynb): Build an information gathering chatbot  
 - [Code Assistant](code_assistant/langgraph_code_assistant.ipynb): Building a code analysis and generation assistant
-- [Web Navigation](web-navigation/web_voyager.ipynb): Building an agent that can navigate and interact with websites
 
 
 #### Multi-Agent Systems
@@ -52,7 +35,6 @@ Learn from example implementations of graphs designed for specific scenarios and
     - [Corrective RAG with local models](rag/langgraph_crag_local.ipynb)
 - [Self-RAG](rag/langgraph_self_rag.ipynb)
     - [Self-RAG with local models](rag/langgraph_self_rag_local.ipynb)
-
 - [Web Research (STORM)](storm/storm.ipynb): Generating Wikipedia-like articles via research and multi-perspective QA
 
 
@@ -69,11 +51,16 @@ Learn from example implementations of graphs designed for specific scenarios and
 - [Language Agent Tree Search](lats/lats.ipynb): Using reflection and rewards to drive a tree search over agents
 - [Self-Discovering Agent](self-discover/self-discover.ipynb): Analyzing an agent that learns about its own capabilities
 
+#### Evaluation
+
+- [Agent-based](chatbot-simulation-evaluation/agent-simulation-evaluation.ipynb): Evaluating chatbots via simulated user interactions
+- [Within LangSmith](chatbot-simulation-evaluation/langsmith-agent-simulation-evaluation.ipynb): Evaluating chatbots in LangSmith over a dialog dataset
+
 #### Competitve Programming
 
 - [Can Language Models Solve Olympiad Programming?](usaco/usaco.ipynb): Build an agent with few-shot "episodic memory" and human-in-the-loop collaboration to solve problems from the USA Computing Olympiad; adapted from the [paper of the same name](https://arxiv.org/abs/2404.10952v1) by Shi, Tang, Narasimhan, and Yao.
 
-#### Evaluation
 
-- [Agent-based](chatbot-simulation-evaluation/agent-simulation-evaluation.ipynb): Evaluating chatbots via simulated user interactions
-- [Dataset-based](chatbot-simulation-evaluation/langsmith-agent-simulation-evaluation.ipynb): Evaluating chatbots in LangSmith over a dialog dataset
+#### Other Experimental Architectures
+
+- [Web Navigation](web-navigation/web_voyager.ipynb): Building an agent that can navigate and interact with websites

docs/mkdocs.yml

@@ -89,19 +89,11 @@ nav:
   - Tutorials:
       - 'tutorials/index.md'
       - Introduction: tutorials/introduction.ipynb
-      - Agent Executor:
-            - "Base": tutorials/agent_executor/base.ipynb
-            - "High-Level": tutorials/agent_executor/high-level.ipynb
-      - Chat Agent Executor:
-            - "Base": tutorials/chat_agent_executor_with_function_calling/base.ipynb
-            - "High-Level": tutorials/chat_agent_executor_with_function_calling/high-level.ipynb
-            - "Tool Node": tutorials/chat_agent_executor_with_function_calling/prebuilt-tool-node.ipynb
-            - "High-Level Tools": tutorials/chat_agent_executor_with_function_calling/high-level-tools.ipynb
       - Use cases:
           - Chatbots:
               - Customer Support: tutorials/customer-support/customer-support.ipynb
               - Info Gathering: tutorials/chatbots/information-gather-prompting.ipynb
-          - Code Assistant: tutorials/code_assistant/langgraph_code_assistant.ipynb
+              - Code Assistant: tutorials/code_assistant/langgraph_code_assistant.ipynb
           - Multi-Agent Systems:
               - Collaboration: tutorials/multi_agent/multi-agent-collaboration.ipynb
               - Supervision: tutorials/multi_agent/agent_supervisor.ipynb

langgraph/checkpoint/memory.py

@@ -22,6 +22,7 @@ class MemorySaver(BaseCheckpointSaver):
         serde (Optional[SerializerProtocol]): The serializer to use for serializing and deserializing checkpoints. Defaults to None.
 
     Examples:
+
             import asyncio
 
             from langgraph.checkpoint.memory import MemorySaver