Agent Trac in g and Debugging with LangSmith: Run Trees, Latency Analysis, and Error Diagnosis

Agent execution is complex: multiple reasoning steps, tool calls, retries. LangSmith traces the entire execution tree, showing which tools were called, their latencies, and any errors. This visibility is critical for debugging and optimization.

Logging to LangSmith

from langsmith import Client
from langchain.agents import initialize_agent
client = Client()
agent = initialize_agent( tools=[search_tool, calculate_tool], llm=llm, agent=AgentType.REACT_DOCSTRING, )
# All runs are automatically logged to LangSmith result = agent.run("What is 5 + 3?") 

LangSmith captures the entire agent tree and displays it in the web UI.

Conclusion

LangSmith provides visibility into agent behavior. Understanding execution flow, tool latencies, and failure points enables rapid iteration. Observability is essential for production agents. Next: managing agent state and memory for multi-turn conversations.