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Add the Docs MCP to your AI coding tool to get help building with Respan. No API key needed.
{
  "mcpServers": {
    "respan-docs": {
      "url": "https://docs.respan.ai/mcp"
    }
  }
}

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What is BeeAI?

BeeAI is IBM’s open-source framework for building AI agents. It provides a modular architecture for creating agents with tool use, memory, and structured workflows.

​
Setup

1

Install packages

pip install beeai-framework respan-ai openinference-instrumentation-beeai python-dotenv
2

Set environment variables

export RESPAN_API_KEY="YOUR_RESPAN_API_KEY"
export OPENAI_API_KEY="YOUR_OPENAI_API_KEY"
3

Initialize and run

import os
from dotenv import load_dotenv

load_dotenv()

from respan import Respan
from openinference.instrumentation.beeai import BeeAIInstrumentor
from beeai import BeeAgent
from beeai.llms.openai import OpenAIChatLLM

# Initialize Respan with BeeAI instrumentation
respan = Respan(instrumentations=[BeeAIInstrumentor()])

llm = OpenAIChatLLM(model="gpt-4.1-nano")

agent = BeeAgent(
    llm=llm,
    instructions="You are a helpful assistant.",
)

response = agent.run("What is the capital of France?")
print(response)
respan.flush()
4

View your trace

Open the Traces page to see your agent trace with LLM calls and tool spans.

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Configuration

ParameterTypeDefaultDescription
api_keystr | NoneNoneFalls back to RESPAN_API_KEY env var.
base_urlstr | NoneNoneFalls back to RESPAN_BASE_URL env var.
instrumentationslist[]Plugin instrumentations to activate (e.g. BeeAIInstrumentor()).
is_auto_instrumentbool | NoneFalseAuto-discover and activate all installed instrumentors via OpenTelemetry entry points.
customer_identifierstr | NoneNoneDefault customer identifier for all spans.
metadatadict | NoneNoneDefault metadata attached to all spans.
environmentstr | NoneNoneEnvironment tag (e.g. "production").

​
Attributes

​
In Respan()

Set defaults at initialization — these apply to all spans. 
from respan import Respan
from openinference.instrumentation.beeai import BeeAIInstrumentor

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

​
With propagate_attributes

Override per-request using a context manager. 
from respan import Respan, workflow, propagate_attributes
from openinference.instrumentation.beeai import BeeAIInstrumentor

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

@workflow(name="handle_request")
def handle_request(user_id: str, question: str):
    with propagate_attributes(
        customer_identifier=user_id,
        thread_identifier="conv_001",
        metadata={"plan": "pro"},
    ):
        response = agent.run(question)
        print(response)
AttributeTypeDescription
customer_identifierstrIdentifies the end user in Respan analytics.
thread_identifierstrGroups related messages into a conversation.
metadatadictCustom key-value pairs. Merged with default metadata.

​
Decorators

Use @workflow and @task to create structured trace hierarchies. 
from respan import Respan, workflow, task
from openinference.instrumentation.beeai import BeeAIInstrumentor
from beeai import BeeAgent
from beeai.llms.openai import OpenAIChatLLM

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

llm = OpenAIChatLLM(model="gpt-4.1-nano")

@task(name="answer_question")
def answer_question(question: str) -> str:
    agent = BeeAgent(llm=llm, instructions="You are a helpful assistant.")
    return str(agent.run(question))

@workflow(name="qa_pipeline")
def pipeline(question: str):
    answer = answer_question(question)
    print(answer)

pipeline("What are the benefits of LLM observability?")
respan.flush()

​
Examples

​
Basic agent

from beeai import BeeAgent
from beeai.llms.openai import OpenAIChatLLM

llm = OpenAIChatLLM(model="gpt-4.1-nano")

agent = BeeAgent(
    llm=llm,
    instructions="You are a helpful assistant that answers concisely.",
)

response = agent.run("Explain machine learning in one sentence.")
print(response)