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116524e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 | """Example: instrument a GLM agent with Kayba tracing.
Run:
uv run python examples/tracing_glm_example.py
"""
from __future__ import annotations
import os
from dotenv import load_dotenv
from openai import OpenAI
from ace.tracing import configure, start_span, trace
load_dotenv()
# --- Kayba tracing setup ---------------------------------------------------
configure(
api_key=os.environ["KAYBA_SDK_KEY"],
folder="examples",
)
# --- GLM client via Zhipu's OpenAI-compatible endpoint ----------------------
client = OpenAI(
base_url=os.environ["OPENAI_BASE_URL"],
api_key=os.environ["OPENAI_API_KEY"],
)
MODEL = "glm-5.1"
# --- Traced helper functions ------------------------------------------------
@trace(name="llm_call", span_type="LLM")
def llm_call(messages: list[dict[str, str]]) -> str:
"""Send a chat completion to GLM and return the text."""
response = client.chat.completions.create(
model=MODEL,
messages=messages,
temperature=0.7,
)
return response.choices[0].message.content or ""
@trace(name="research_agent")
def research_agent(topic: str) -> str:
"""Agent that gathers key facts about a topic."""
with start_span("build_prompt") as span:
messages = [
{
"role": "system",
"content": "You are a research assistant. List 3 key facts.",
},
{"role": "user", "content": f"Research this topic: {topic}"},
]
span.set_inputs({"topic": topic})
span.set_outputs({"message_count": len(messages)})
result = llm_call(messages)
return result
@trace(name="summariser_agent")
def summariser_agent(facts: str) -> str:
"""Agent that summarises research into a single paragraph."""
with start_span("build_prompt") as span:
messages = [
{
"role": "system",
"content": (
"You are a summariser. Condense the following facts "
"into one concise paragraph."
),
},
{"role": "user", "content": facts},
]
span.set_inputs({"facts_length": len(facts)})
span.set_outputs({"message_count": len(messages)})
result = llm_call(messages)
return result
@trace(name="pipeline")
def run_pipeline(topic: str) -> str:
"""Two-agent pipeline: research → summarise."""
facts = research_agent(topic)
print(f"\n--- Research Agent ---\n{facts}")
summary = summariser_agent(facts)
print(f"\n--- Summariser Agent ---\n{summary}")
return summary
# --- Main -------------------------------------------------------------------
if __name__ == "__main__":
result = run_pipeline("The history of the Silk Road")
print(f"\n--- Final result ---\n{result}")
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