LangGraph vs ReAct Agent

For a ReAct agent, the best practice is to centralize the core reasoning prompt in the main agent.

• The Agent's Role (The "Brain"): The createReactAgent model is designed around a single, central "brain." This brain's job is to manage the entire conversation, maintain context, and decide which tool to use next. Its prompt is the constitution for the entire operation.
• The Tool's Role (The "Hands"): Tools should be as "dumb" and deterministic as possible. They are the hands that execute specific, well-defined tasks.
• The Problem with Prompts in Tools (in this model): When you embed a powerful LLM prompt inside a tool, you are essentially creating a "secret brain" that the main agent knows nothing about. The main agent decides to call generateSql, but it has no visibility into how that tool reasons. If the tool's internal prompt fails, the main agent is helpless. It can't correct the tool's reasoning because it can't see it. It just knows the tool failed. This makes the system brittle.

In a LangGraph, it is not only acceptable but often preferable to have nodes that contain their own LLM prompts.

1. The Graph is the Orchestrator: In a custom LangGraph, the high-level reasoning is no longer in a single prompt. The reasoning is encoded in the graph's structure itself—the nodes and the edges that connect them. The graph is the explicit workflow. You are replacing the implicit reasoning of a single ReAct prompt with the explicit logic of a state machine.

2. Nodes as Specialized Agents: Because the graph handles the high-level orchestration, each node can be thought of as a highly specialized, single-purpose agent.

   • The generate_sql node's only job is to convert a natural language query and a schema into a SQL query. It's perfectly acceptable for it to use a dedicated, fine-tuned LLM prompt to do this one job well.
   • The process_results node can have its own LLM prompt to translate a raw CSV path or an error message into a friendly, conversational response.

3. Control and Observability: The key difference is control. If the generate_sql node fails, the graph's structure (via conditional edges) knows exactly what happened and can decide what to do next:

   • Route to an error-handling node.
   • Route back to the generate_sql node with instructions to try again.
   • Ask the user for more information.

The main orchestrator (the graph) never loses control, even if a node's internal LLM call fails.

Choose createReactAgent for:

• Rapid Prototyping: When you need to get a simple demo working quickly.
• Simple Conversational Bots: For agents that don't follow a rigid, multi-step process and are more about general conversation.
• Low-Stakes Tasks: Where occasional failures or incorrect behavior are acceptable.

Choose a Custom LangGraph for:

• Production-Grade Systems: When reliability and predictability are critical.
• Task-Oriented Agents: For any agent that must follow a specific, repeatable workflow to accomplish a task (like the query-db-agent).
• Complex Error Handling: When you need to build intelligent recovery and self-correction loops.
• Cost Optimization: When you need to control context size at each step to manage API costs effectively.