docs: small cleanup (#733)

docs/docs/how-tos/index.md

@@ -4,37 +4,34 @@ Welcome to the LangGraph how-to guides! These guides provide practical, step-by-
 
 ## Core
 
-The core guides show how to address common needs when building out AI workflows, with special focus placed on [ReAct](https://arxiv.org/abs/2210.03629)-style agents with [tool calling](https://python.langchain.com/docs/modules/model_io/chat/function_calling/).
+The core guides show how to address common needs when building out AI workflows, with special focus placed on [ReAct](https://arxiv.org/abs/2210.03629)-style agents with [tool calling](https://python.langchain.com/docs/modules/model_io/chat/function_calling/) (agents that <strong>Re</strong>ason and **Act** to accomplish tasks).
 
-- [ReAct agent](create-react-agent.ipynb): How to create a tool-calling agent that **Re**asons and **Act**s to accomplish tasks
-- [Persistence](persistence.ipynb): How to give your graph "memory" and resilience by saving and loading state
-- [Time travel](time-travel.ipynb): How to navigate and manipulate graph state history once it's persisted
-- [Async execution](async.ipynb): How to run nodes asynchronously for improved performance
-- [Streaming responses](streaming-tokens.ipynb): How to stream agent responses in real-time
-- [Visualization](visualization.ipynb): How to visualize your graphs
-- [Configuration](configuration.ipynb): How to indicate that a graph can swap out configurable components
+- [How to create a ReAct agent](create-react-agent.ipynb)
+- [How to add persistence ("memory") to your graph](persistence.ipynb)
+- [How to view and update graph state](time-travel.ipynb)
+- [How to run graph asynchronously](async.ipynb)
+- [How to stream graph responses](streaming-tokens.ipynb)
+- [How to visualize your graph](visualization.ipynb)
+- [How to add runtime configuration to your graph](configuration.ipynb)
 
 ### Design patterns
 
 Recipes showing how to apply common design patterns in your workflows:
 
-- [Subgraphs](subgraph.ipynb): How to compose subgraphs within a larger graph
-- [Branching](branching.ipynb): How to create branching logic in your graphs for parallel node execution
-- [Map-reduce](map-reduce.ipynb): How to branch **different views** of the state for parallel node execution (even applying the same node in parallel N times)
-- [Human-in-the-loop](human-in-the-loop.ipynb): How to incorporate human feedback and intervention
+- [How to create subgraphs](subgraph.ipynb)
+- [How to create branches for parallel execution](branching.ipynb)
+- [How to create map-reduce branches for parallel execution](map-reduce.ipynb)
+- [How to add human-in-the-loop](human-in-the-loop.ipynb)
 
-The following examples are useful especially if you are used to LangChain's AgentExecutor configurations.
+The following examples are useful especially if you are used to LangChain's `AgentExecutor` configurations.
 
-- [Force calling a tool first](force-calling-a-tool-first.ipynb): Define a fixed workflow before ceding control to the ReAct agent
-- [Pass run time values to tools](pass-run-time-values-to-tools.ipynb): Pass values that are only known at run time to tools (e.g., the ID of the user who made the request)
-- [Dynamic direct return](dynamically-returning-directly.ipynb): Let the LLM decide whether the graph should finish after a tool is run or whether the LLM should be able to review the output and keep going
-- [Respond in structured format](respond-in-format.ipynb): Let the LLM use tools or populate schema to provide the user. Useful if your agent should generate structured content
-- [Managing agent steps](managing-agent-steps.ipynb): How to format the intermediate steps of your workflow for the agent
+- [How to force an agent to call a tool](force-calling-a-tool-first.ipynb)
+- [How to pass runtime values to tools](pass-run-time-values-to-tools.ipynb)
+- [How to let agent return tool results directly](dynamically-returning-directly.ipynb)
+- [How to have agent respond in structured format](respond-in-format.ipynb)
+- [How to manage agent steps](managing-agent-steps.ipynb)
 
-### Alternative ways to define state
+### Advanced
 
-- [Pydantic state](state-model.ipynb): Use a Pydantic model as your state
-
-### Structured output
-
-- [Extraction with re-prompting](./extraction/retries.ipynb): How to generate complex nested schemas using JSONPatch retries, for when function calling is insufficient, and regular reprompting still fails to generate valid results
+- [How to use a Pydantic model as your state](state-model.ipynb)
+- [How to extract structured output with re-prompting](./extraction/retries.ipynb)

docs/docs/tutorials/index.md

@@ -15,55 +15,47 @@ Learn from example implementations of graphs designed for specific scenarios and
 #### Chatbots
 
 - [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
-
+- [Prompt Generation from User Requirements](chatbots/information-gather-prompting.ipynb): Build an information gathering chatbot
+- [Code Assistant](code_assistant/langgraph_code_assistant.ipynb): Build a code analysis and generation assistant
 
 #### Multi-Agent Systems
 
-- [Collaboration](multi_agent/multi-agent-collaboration.ipynb): Enabling two agents to collaborate on a task  
-- [Supervision](multi_agent/agent_supervisor.ipynb): Using an LLM to orchestrate and delegate to individual agents
-- [Hierarchical Teams](multi_agent/hierarchical_agent_teams.ipynb): Orchestrating nested teams of agents to solve problems
+- [Collaboration](multi_agent/multi-agent-collaboration.ipynb): Enable two agents to collaborate on a task
+- [Supervision](multi_agent/agent_supervisor.ipynb): Use an LLM to orchestrate and delegate to individual agents
+- [Hierarchical Teams](multi_agent/hierarchical_agent_teams.ipynb): Orchestrate nested teams of agents to solve problems
 
 #### RAG
 
 - [Adaptive RAG](rag/langgraph_adaptive_rag.ipynb)
-    - [Adaptive RAG using local models](rag/langgraph_adaptive_rag_local.ipynb)
-- [Agentic RAG.ipynb](rag/langgraph_agentic_rag.ipynb)
+    - [Adaptive RAG using local LLMs](rag/langgraph_adaptive_rag_local.ipynb)
+- [Agentic RAG](rag/langgraph_agentic_rag.ipynb)
 - [Corrective RAG](rag/langgraph_crag.ipynb)
-    - [Corrective RAG with local models](rag/langgraph_crag_local.ipynb)
+    - [Corrective RAG using local LLMs](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
-
+    - [Self-RAG using local LLMs](rag/langgraph_self_rag_local.ipynb)
+- [SQL Agent](sql-agent.ipynb)
 
 #### Planning Agents
 
-- [Plan-and-Execute](plan-and-execute/plan-and-execute.ipynb): Implementing a basic planning and execution agent  
-- [Reasoning without Observation](rewoo/rewoo.ipynb): Reducing re-planning by saving observations as variables 
-- [LLMCompiler](llm-compiler/LLMCompiler.ipynb): Streaming and eagerly executing a DAG of tasks from a planner
+- [Plan-and-Execute](plan-and-execute/plan-and-execute.ipynb): Implement a basic planning and execution agent
+- [Reasoning without Observation](rewoo/rewoo.ipynb): Reduce re-planning by saving observations as variables
+- [LLMCompiler](llm-compiler/LLMCompiler.ipynb): Stream and eagerly execute a DAG of tasks from a planner
 
 #### Reflection & Critique 
 
-- [Basic Reflection](reflection/reflection.ipynb): Prompting the agent to reflect on and revise its outputs
-- [Reflexion](reflexion/reflexion.ipynb): Critiquing missing and superfluous details to guide next steps
-- [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
+- [Basic Reflection](reflection/reflection.ipynb): Prompt the agent to reflect on and revise its outputs
+- [Reflexion](reflexion/reflexion.ipynb): Critique missing and superfluous details to guide next steps
+- [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
 
 #### 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
+- [Agent-based](chatbot-simulation-evaluation/agent-simulation-evaluation.ipynb): Evaluate chatbots via simulated user interactions
+- [In LangSmith](chatbot-simulation-evaluation/langsmith-agent-simulation-evaluation.ipynb): Evaluate chatbots in LangSmith over a dialog dataset
 
-#### Text Mining
+#### Experimental
 
-- [TNT-LLM](tnt-llm/tnt-llm.ipynb): learn to build rich, interpretable taxonomies of user intentand using the classification system developed by Microsoft for their Bing Copilot application.
-
-#### Competitive 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.
-
-
-#### Other Experimental Architectures
-
-- [Web Navigation](web-navigation/web_voyager.ipynb): Building an agent that can navigate and interact with websites
+- [Web Research (STORM)](storm/storm.ipynb): Generate Wikipedia-like articles via research and multi-perspective QA
+- [TNT-LLM](tnt-llm/tnt-llm.ipynb): Build rich, interpretable taxonomies of user intentand using the classification system developed by Microsoft for their Bing Copilot application.
+- [Web Navigation](web-navigation/web_voyager.ipynb): Build an agent that can navigate and interact with websites
+- [Competitive 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 ["Can Language Models Solve Olympiad Programming?"](https://arxiv.org/abs/2404.10952v1) paper by Shi, Tang, Narasimhan, and Yao.