name: langchain-agents description: Building LLM agents with LangChain and LangGraph, covering tool-calling model initialization, state management, and observability with LangSmith. Triggers: langchain, langgraph, langsmith, agent-executor, chat-model-tools.

LangChain Agents

Overview

LangChain provides a standard interface for building LLM agents that can use tools. Modern agent development is moving toward LangGraph to handle stateful, multi-turn, and non-linear logic that simple loops cannot capture.

When to Use

• Multi-Provider Apps: When you want to swap between OpenAI, Anthropic, and local models without changing business logic.
• Stateful Agents: When you need human-in-the-loop, long-term persistence, or complex execution graphs.
• Observability: Using LangSmith to debug exactly where an agentic chain failed.

Decision Tree

1. Is it a simple tool-calling loop?

• YES: Use the create_agent abstraction.

1. Does it require cycles, complex state transitions, or human approval?

• YES: Build using LangGraph.

1. Do you need to track exactly how much an agent run cost or where it halluncinated?

• YES: Enable LangSmith tracing.

Workflows

1. Creating a Simple Tool-Enabled Agent

1. Define a Python function with a docstring to serve as a tool.
2. Initialize a ChatModel (e.g., ChatAnthropic).
3. Call create_agent(model, tools=[...]) to generate the agent.
4. Execute the agent using agent.invoke({"messages": [...]}).

2. Debugging with LangSmith

1. Enable LangSmith environment variables (LANGSMITH_API_KEY, LANGSMITH_TRACING).
2. Run the agent as usual; traces are automatically captured.
3. Visualize the execution path and captured state transitions in the LangSmith UI to identify where the agent went wrong.

3. Adding Memory to an Agent

1. Initialize a Checkpointer or Memory object.
2. Pass the memory to the agent's invoke call to maintain state across turns.
3. The agent will automatically append tool outputs and model responses to the conversation thread.

Non-Obvious Insights

• Abstraction Layer: LangChain's primary value is standardizing the interface across providers, preventing vendor lock-in.
• Simple vs. Complex: For many basic tasks, you don't need LangGraph; the high-level agent_executor is often enough for under 10 lines of code.
• Durable Execution: Using LangGraph allows for "checkpoints," meaning an agent can stop, wait for human input, and resume hours later without losing state.

Evidence

• "Standardizes how you interact with models so that you can seamlessly swap providers..." - LangChain Docs
• "LangChain agents are built on top of LangGraph in order to provide durable execution, streaming... persistence." - LangChain Docs
• "You do not need to know LangGraph for basic LangChain agent usage." - LangChain Docs

Scripts

• scripts/langchain-agents_tool.py: Python script for tool definition and agent invocation.
• scripts/langchain-agents_tool.js: Equivalent logic using LangChain.js.

Dependencies

• langchain
• langgraph
• langsmith

References

• references/README.md