Practical Application Building a Basic Conversational Agent

• Easy to visualize,
• Highly modular, and
• A practical, real-world use case you can adapt for your organization.

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High-level objective

• Build a chatbot that:
  • Understands user intent,
  • Decides whether to answer directly or perform a document search, and
  • Returns a formatted response.
• Adhere to LangGraph principles: modular nodes, explicit state, and declarative routing.

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High-level flow

1. Receive user input — an entry node persists the user’s message into the graph state.
2. Classify intent — a lightweight LLM or classifier marks whether the user expects a direct answer or a search.
3. Conditional routing — route to search or answer branches based on intent.
4. External search (optional) — perform retrieval when external knowledge is required.
5. Answer generation — use an LLM to produce the response (with or without retrieved context).
6. Merge and respond — format a final response in a shared node so output formatting or streaming can be centralized.

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About the entry node and state

• The entry node’s responsibility is to persist the raw user message into graph state (for example: state.user_message).
• Every subsequent node reads from and writes to state; this keeps each node small, composable, and independently testable.

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Routing and branching

• After intent classification, treat the graph like a decision tree: if intent == "search", route to the retrieval branch; if intent == "answer", route to the direct answer branch.
• Converge both branches into a common response node. Centralizing response formatting makes it trivial to add streaming, templating, or channel-specific outputs without changing upstream logic.
• Because nodes are independent and routing is declarative, you can swap or instrument nodes without refactoring unrelated components.

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Extensibility and observability

• Replace the intent classifier with a fine-tuned model when higher accuracy is required.
• Add a fallback node to handle empty search results or low-confidence classifications.
• Swap the final response generator for a streaming responder to enable real-time outputs.
• Use LangGraph observability tools (logs, metrics, traces) to debug node behavior and routing decisions.

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Quick reference: Node responsibilities and example state

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Minimal node pseudocode

// Entry node
function entryNode(input, state) {
  state.user_message = input;
  return state;
}

// Intent classifier
function classifyIntent(state) {
  // simple LLM/classifier call returns "search" or "answer"
  state.intent = callClassifier(state.user_message);
  return state;
}

// Conditional routing handled by LangGraph's declarative edges
// Retrieval node (runs only if state.intent === "search")
function retrieve(state) {
  state.search_results = callRetriever(state.user_message);
  return state;
}

// Generator (uses search_results if present)
function generateAnswer(state) {
  const context = state.search_results || null;
  state.answer = callLLM(state.user_message, context);
  return state;
}

// Final formatter
function respond(state) {
  state.formatted_response = formatTemplate(state.answer, state.search_results);
  return state.formatted_response;
}

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Recap

• Nodes are chainable functions: LLM calls, retrievers, classifiers, and formatters.
• State captures user input and intermediate results, enabling small, testable nodes.
• Conditional routing makes flow explicit and predictable.
• The pattern is readable, testable, reusable, and ready to extend with cycles, memory, or streaming.

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Links and references

• LangGraph documentation
• Retrieval-Augmented Generation patterns
• Best practices for prompt design and LLM safety