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BeeAI

BeeAI is an open-source platform that enables developers to discover, run, and compose AI agents from any framework, facilitating the creation of interoperable multi-agent systems

Website: https://github.com/i-am-bee/beeai-platform

BeeAI Tracing (JS)

BeeAI Tracing (Python)

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BeeAI Tracing (JS)

Auto-instrument and observe BeeAI agents

This module provides automatic instrumentation for . It integrates seamlessly with the package to collect and export telemetry data.

Install

Setup

To instrument your application, import and enable BeeAIInstrumentation. Create the instrumentation.js file:

Run BeeAI

Sample agent built using BeeAI with automatic tracing:

Observe

Phoenix provides visibility into your BeeAI agent operations by automatically tracing all interactions.

Troubleshooting

Add the following at the top of your instrumentation.js to see OpenTelemetry diagnostic logs in your console while debugging:

If traces aren't appearing, a common cause is an outdated beeai-framework package. Check the diagnostic logs for version or initialization errors and update your package as needed.

Custom Tracer Provider

You can specify a custom tracer provider for BeeAI instrumentation in multiple ways:

Method 1: Pass tracerProvider on instantiation

Method 2: Set tracerProvider after instantiation

Method 3: Pass tracerProvider to registerInstrumentations

Resources

npm install --save beeai-framework \ 
  @arizeai/openinference-instrumentation-beeai \
  @arizeai/openinference-semantic-conventions \
  @opentelemetry/sdk-trace-node \
  @opentelemetry/resources \
  @opentelemetry/exporter-trace-otlp-proto \
  @opentelemetry/semantic-conventions \
  @opentelemetry/instrumentation
import {
  NodeTracerProvider,
  SimpleSpanProcessor,
  ConsoleSpanExporter,
} from "@opentelemetry/sdk-trace-node";
import { diag, DiagConsoleLogger, DiagLogLevel } from "@opentelemetry/api";
import { resourceFromAttributes } from "@opentelemetry/resources";
import { OTLPTraceExporter } from "@opentelemetry/exporter-trace-otlp-proto";
import { ATTR_SERVICE_NAME } from "@opentelemetry/semantic-conventions";
import { SEMRESATTRS_PROJECT_NAME } from "@arizeai/openinference-semantic-conventions";
import * as beeaiFramework from "beeai-framework";
import { registerInstrumentations } from "@opentelemetry/instrumentation";
import { BeeAIInstrumentation } from "@arizeai/openinference-instrumentation-beeai";

const COLLECTOR_ENDPOINT = "your-phoenix-collector-endpoint";

const provider = new NodeTracerProvider({
  resource: resourceFromAttributes({
    [ATTR_SERVICE_NAME]: "beeai-project",
    [SEMRESATTRS_PROJECT_NAME]: "beeai-project",
  }),
  spanProcessors: [
    new SimpleSpanProcessor(new ConsoleSpanExporter()),
    new SimpleSpanProcessor(
      new OTLPTraceExporter({
        url: `${COLLECTOR_ENDPOINT}/v1/traces`,
        // (optional) if connecting to Phoenix with Authentication enabled
        headers: { Authorization: `Bearer ${process.env.PHOENIX_API_KEY}` },
      }),
    ),
  ],
});

provider.register();

const beeAIInstrumentation = new BeeAIInstrumentation();
beeAIInstrumentation.manuallyInstrument(beeaiFramework);

registerInstrumentations({
  instrumentations: [beeAIInstrumentation],
});

console.log("👀 OpenInference initialized");
import "./instrumentation.js";
import { ToolCallingAgent } from "beeai-framework/agents/toolCalling/agent";
import { TokenMemory } from "beeai-framework/memory/tokenMemory";
import { DuckDuckGoSearchTool } from "beeai-framework/tools/search/duckDuckGoSearch";
import { OpenMeteoTool } from "beeai-framework/tools/weather/openMeteo";
import { OpenAIChatModel } from "beeai-framework/adapters/openai/backend/chat";

const llm = new OpenAIChatModel(
  "gpt-4o", 
  {},
  { apiKey: 'your-openai-api-key' }
);

const agent = new ToolCallingAgent({
  llm,
  memory: new TokenMemory(),
  tools: [
    new DuckDuckGoSearchTool(),
    new OpenMeteoTool(), // weather tool
  ],
});

async function main() {
  const response = await agent.run({ prompt: "What's the current weather in Berlin?" });
  console.log(`Agent 🤖 : `, response.result.text);
}

main();
import { diag, DiagConsoleLogger, DiagLogLevel } from "@opentelemetry/api";

// Enable OpenTelemetry diagnostic logging
diag.setLogger(new DiagConsoleLogger(), DiagLogLevel.INFO);
const beeAIInstrumentation = new BeeAIInstrumentation({
  tracerProvider: customTracerProvider,
});
beeAIInstrumentation.manuallyInstrument(beeaiFramework);
const beeAIInstrumentation = new BeeAIInstrumentation();
beeAIInstrumentation.setTracerProvider(customTracerProvider);
beeAIInstrumentation.manuallyInstrument(beeaiFramework);
const beeAIInstrumentation = new BeeAIInstrumentation();
beeAIInstrumentation.manuallyInstrument(beeaiFramework);

registerInstrumentations({
  instrumentations: [beeAIInstrumentation],
  tracerProvider: customTracerProvider,
});
BeeAI framework
@opentelemetry/sdk-trace-node
BeeAI Framework GitHub
OpenInference BeeAI Instrumentation Package
OpenTelemetry Node.js SDK Documentation
BeeAI Examples
BeeAI has native integration with Arize Phoenix
NPM Version

BeeAI Tracing (Python)

Instrument and observe BeeAI agents

Phoenix provides seamless observability and tracing for BeeAI agents through the Python OpenInference instrumentation package.

Launch Phoenix

Install

pip install openinference-instrumentation-beeai beeai-framework

Setup

Connect to your Phoenix instance using the register function.

from phoenix.otel import register

# configure the Phoenix tracer
tracer_provider = register(
  project_name="beeai-agent", # Default is 'default'
  auto_instrument=True # Auto-instrument your app based on installed OI dependencies
)

Run BeeAI

Sample agent built using BeeAI with automatic tracing:

import asyncio
from beeai_framework.agents.react import ReActAgent
from beeai_framework.agents.types import AgentExecutionConfig
from beeai_framework.backend.chat import ChatModel
from beeai_framework.backend.types import ChatModelParameters
from beeai_framework.memory import TokenMemory
from beeai_framework.tools.search.duckduckgo import DuckDuckGoSearchTool
from beeai_framework.tools.search.wikipedia import WikipediaTool
from beeai_framework.tools.tool import AnyTool
from beeai_framework.tools.weather.openmeteo import OpenMeteoTool

llm = ChatModel.from_name(
    "ollama:granite3.1-dense:8b",
    ChatModelParameters(temperature=0),
)

tools: list[AnyTool] = [
    WikipediaTool(),
    OpenMeteoTool(),
    DuckDuckGoSearchTool(),
]

agent = ReActAgent(llm=llm, tools=tools, memory=TokenMemory(llm))

prompt = "What's the current weather in Las Vegas?"

async def main() -> None:
    response = await agent.run(
        prompt=prompt,
        execution=AgentExecutionConfig(
            max_retries_per_step=3, total_max_retries=10, max_iterations=20
        ),
    )

    print("Agent 🤖 : ", response.result.text)
    
asyncio.run(main())

Observe

Phoenix provides visibility into your BeeAI agent operations by automatically tracing all interactions.

Resources

  • OpenInference package for Python

Sign up for Phoenix:

  1. Sign up for an Arize Phoenix account at https://app.phoenix.arize.com/login

  2. Click Create Space, then follow the prompts to create and launch your space.

Install packages:

pip install arize-phoenix-otel

Set your Phoenix endpoint and API Key:

From your new Phoenix Space

  1. Create your API key from the Settings page

  2. Copy your Hostname from the Settings page

  3. In your code, set your endpoint and API key:

import os

os.environ["PHOENIX_API_KEY"] = "ADD YOUR PHOENIX API KEY"
os.environ["PHOENIX_COLLECTOR_ENDPOINT"] = "ADD YOUR PHOENIX HOSTNAME"

# If you created your Phoenix Cloud instance before June 24th, 2025,
# you also need to set the API key as a header:
# os.environ["PHOENIX_CLIENT_HEADERS"] = f"api_key={os.getenv('PHOENIX_API_KEY')}"

Having trouble finding your endpoint? Check out Finding your Phoenix Endpoint

Launch your local Phoenix instance:

pip install arize-phoenix
phoenix serve

For details on customizing a local terminal deployment, see Terminal Setup.

Install packages:

pip install arize-phoenix-otel

Set your Phoenix endpoint:

import os

os.environ["PHOENIX_COLLECTOR_ENDPOINT"] = "http://localhost:6006"

See Terminal for more details.

Pull latest Phoenix image from Docker Hub:

docker pull arizephoenix/phoenix:latest

Run your containerized instance:

docker run -p 6006:6006 arizephoenix/phoenix:latest

This will expose the Phoenix on localhost:6006

Install packages:

pip install arize-phoenix-otel

Set your Phoenix endpoint:

import os

os.environ["PHOENIX_COLLECTOR_ENDPOINT"] = "http://localhost:6006"

For more info on using Phoenix with Docker, see Docker.

Install packages:

pip install arize-phoenix

Launch Phoenix:

import phoenix as px
px.launch_app()

By default, notebook instances do not have persistent storage, so your traces will disappear after the notebook is closed. See self-hosting or use one of the other deployment options to retain traces.