[Docs] Drop memory doc (#1986)

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Co-authored-by: vbarda <vadym@langchain.dev>

docs/docs/tutorials/index.md

@@ -53,10 +53,6 @@ Learn from example implementations of graphs designed for specific scenarios and
 - [Language Agent Tree Search](lats/lats.ipynb): Use reflection and rewards to drive a tree search over agents
 - [Self-Discover Agent](self-discover/self-discover.ipynb): Analyze an agent that learns about its own capabilities
 
-#### Memory
-
-- [Long-term memory](memory/long_term_memory_agent.ipynb): Build an agent that can store, retrieve, and use memories to enhance its interactions with users.
-
 #### Evaluation
 
 - [Agent-based](chatbot-simulation-evaluation/agent-simulation-evaluation.ipynb): Evaluate chatbots via simulated user interactions

docs/mkdocs.yml

@@ -118,8 +118,6 @@ nav:
               - Reflexion: tutorials/reflexion/reflexion.ipynb
               - Language Agent Tree Search: tutorials/lats/lats.ipynb
               - Self-Discover Agent: tutorials/self-discover/self-discover.ipynb
-      - Memory:
-          - Long-term memory: tutorials/memory/long_term_memory_agent.ipynb
       - Evaluation & Analysis:
           - Chatbot Evaluation via Simulation:
               - Agent-based: tutorials/chatbot-simulation-evaluation/agent-simulation-evaluation.ipynb