Agent Observability

Agent Observability and Tracing

# LangSmith tracing (LangChain)
import os
os.environ["LANGCHAIN_TRACING_V2"] = "true"
os.environ["LANGCHAIN_API_KEY"]     = "ls-your-key"
os.environ["LANGCHAIN_PROJECT"]     = "my-agent"
# All agent steps auto-traced

# Langfuse for framework-agnostic tracing
from langfuse.decorators import observe, langfuse_context
import time

@observe(name="agent_run")
def run_agent_traced(goal: str) -> str:
    langfuse_context.update_current_observation(
        input=goal
    )
    result = run_agent(goal)
    langfuse_context.update_current_observation(
        output=result,
        metadata={"turns": agent_turns}
    )
    return result

@observe(name="tool_call")
def traced_tool(name: str, args: dict) -> str:
    start  = time.time()
    result = execute_tool(name, args)
    langfuse_context.update_current_observation(
        input=args, output=result,
        metadata={"tool":name,"latency":time.time()-start}
    )
    return result

# Key metrics: tool success rate, avg turns per task
# Token usage per task, failure patterns, latency