BeeAI (tracing)

BeeAI is an open-source framework for building production-ready AI agents with memory, tools, and LLM integrations. Respan gives you visibility into BeeAI workflow spans, agent runs, LLM calls, and tool calls, plus gateway routing through the OpenAI-compatible Respan endpoint.

Set up Respan

Create an account at platform.respan.ai and grab an API key .

Paste this into your coding agent:


Help me set up Respan tracing with the BeeAI integration.
Follow https://www.respan.ai/docs/integrations/bee-ai.md

Setup

Install packages

Terminal


pip install respan-ai respan-instrumentation-beeai beeai-framework

@arizeai/openinference-instrumentation-beeai currently instruments beeai-framework >=0.1.9 <0.1.14. Use beeai-framework@0.1.13 for compatible TypeScript tracing.

Set environment variables

Terminal


export OPENAI_API_KEY="YOUR_OPENAI_API_KEY"
export RESPAN_API_KEY="YOUR_RESPAN_API_KEY"

Python uses OPENAI_API_KEY for LLM requests and RESPAN_API_KEY for trace export. TypeScript can route LLM calls through the Respan gateway with RESPAN_API_KEY, or you can set RESPAN_GATEWAY_API_KEY separately.

Initialize and run

Python


import asyncio
import os
from dotenv import load_dotenv

load_dotenv()

from beeai_framework.agents.requirement import RequirementAgent
from beeai_framework.backend import ChatModel
from respan import Respan
from respan_instrumentation_beeai import BeeAIInstrumentor

respan = Respan(
    app_name="beeai-python-example",
    instrumentations=[BeeAIInstrumentor()],
)


async def main() -> None:
    agent = RequirementAgent(
        llm=ChatModel.from_name(os.getenv("BEEAI_MODEL", "openai:gpt-4.1-nano")),
        role="observability assistant",
        instructions="Answer clearly and concisely.",
    )
    response = await agent.run(
        "Explain why traces help agent debugging in one sentence."
    )
    print(response.last_message.text)


asyncio.run(main())

View your trace

Open the Traces page to see your BeeAI workflow with agent, chat, and tool spans.

Configuration

Parameter	Type	Default	Description
`api_key` / `apiKey`	`str \| None` / `string \| undefined`	`RESPAN_API_KEY`	Respan API key for trace export.
`base_url` / `baseURL`	`str \| None` / `string \| undefined`	`RESPAN_BASE_URL`	Respan trace export base URL.
`instrumentations`	`list` / `RespanInstrumentation[]`	`[]`	Plugin instrumentations to activate, such as `BeeAIInstrumentor()` or `new BeeAIInstrumentor({ sdkModule })`.
`app_name` / `appName`	`str \| None` / `string \| undefined`	`None` / `undefined`	Service name shown on exported spans.
`metadata`	`dict \| None` / `Record<string, unknown>`	`None` / `undefined`	Default metadata attached to exported spans.
`environment`	`str \| None` / `string \| undefined`	`None` / `undefined`	Environment tag, such as `"production"`.

Attributes

In Respan()

Set defaults at initialization. These apply to exported spans.

Python


from respan import Respan
from respan_instrumentation_beeai import BeeAIInstrumentor

respan = Respan(
    app_name="beeai-agent-api",
    instrumentations=[BeeAIInstrumentor()],
    customer_identifier="user_123",
    metadata={"service": "beeai-api", "version": "1.0.0"},
)

With propagate_attributes

Override per-request using a context scope.

Python


from respan import Respan, propagate_attributes
from respan_instrumentation_beeai import BeeAIInstrumentor

respan = Respan(instrumentations=[BeeAIInstrumentor()])


async def handle_request(user_id: str, message: str):
    with propagate_attributes(
        customer_identifier=user_id,
        thread_identifier="conv_abc_123",
        metadata={"plan": "pro"},
    ):
        response = await agent.run(message)
        print(response.last_message.text)

Attribute	Type	Description
`customer_identifier`	`str`	Identifies the end user in Respan analytics.
`thread_identifier`	`str`	Groups related messages into a conversation.
`metadata`	`dict`	Custom key-value pairs. Merged with default metadata.