Documentation Index
Fetch the complete documentation index at: /llms.txt
Use this file to discover all available pages before exploring further.
Automatically track OpenAI API calls
OpenAI and AsyncOpenAI clients.
import agentbasis
from agentbasis.llms.openai import instrument
# Initialize AgentBasis first
agentbasis.init(api_key="your-api-key", agent_id="your-agent-id")
# Enable OpenAI instrumentation (covers sync and async)
instrument()
instrument() call instruments both synchronous and asynchronous clients. You don’t need to call it twice.from openai import OpenAI
client = OpenAI()
response = client.chat.completions.create(
model="gpt-4",
messages=[{"role": "user", "content": "Hello world"}]
)
print(response.choices[0].message.content)
from openai import AsyncOpenAI
import asyncio
async def main():
client = AsyncOpenAI()
response = await client.chat.completions.create(
model="gpt-4",
messages=[{"role": "user", "content": "Hello world"}]
)
print(response.choices[0].message.content)
asyncio.run(main())
stream = client.chat.completions.create(
model="gpt-4",
messages=[{"role": "user", "content": "Tell me a story"}],
stream=True,
)
for chunk in stream:
print(chunk.choices[0].delta.content or "", end="")
async def stream_response():
client = AsyncOpenAI()
stream = await client.chat.completions.create(
model="gpt-4",
messages=[{"role": "user", "content": "Tell me a story"}],
stream=True,
)
async for chunk in stream:
print(chunk.choices[0].delta.content or "", end="")
| Field | Description |
|---|---|
gen_ai.system | openai |
gen_ai.request.model | Model name (e.g., gpt-4) |
gen_ai.prompt | Input messages |
gen_ai.completion | Response content |
gen_ai.usage.prompt_tokens | Prompt token count |
gen_ai.usage.completion_tokens | Completion token count |
duration | Request latency |