> ## Documentation Index
> Fetch the complete documentation index at: https://docs.agentops.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# AG2
> Track and analyze your AG2 agents with AgentOps
[AG2](https://ag2.ai/) (formerly AutoGen) is a framework for building multi-agent conversational AI systems. AgentOps provides seamless, automatic instrumentation for AG2 — just call `agentops.init()` and all agent interactions are tracked.
## Installation
```bash pip theme={null}
pip install agentops pyautogen
```
```bash poetry theme={null}
poetry add agentops pyautogen
```
```bash uv theme={null}
uv pip install agentops pyautogen
```
## Setting Up API Keys
Before using AG2 with AgentOps, you need to set up your API keys. You can obtain:
* **OPENAI\_API\_KEY**: From the [OpenAI Platform](https://platform.openai.com/api-keys)
* **AGENTOPS\_API\_KEY**: From your [AgentOps Dashboard](https://app.agentops.ai/)
Then to set them up, you can either export them as environment variables or set them in a `.env` file.
```bash Export to CLI theme={null}
export OPENAI_API_KEY="your_openai_api_key_here"
export AGENTOPS_API_KEY="your_agentops_api_key_here"
```
```txt Set in .env file theme={null}
OPENAI_API_KEY="your_openai_api_key_here"
AGENTOPS_API_KEY="your_agentops_api_key_here"
```
Then load the environment variables in your Python code:
```python theme={null}
from dotenv import load_dotenv
import os
# Load environment variables from .env file
load_dotenv()
# Set up environment variables with fallback values
os.environ["OPENAI_API_KEY"] = os.getenv("OPENAI_API_KEY")
os.environ["AGENTOPS_API_KEY"] = os.getenv("AGENTOPS_API_KEY")
```
## Usage
Initialize AgentOps at the beginning of your application to automatically track all AG2 agent interactions:
```python Single Agent Conversation theme={null}
import agentops
import autogen
import os
# Initialize AgentOps
agentops.init()
# Configure your AG2 agents
config_list = [
{
"model": "gpt-4",
"api_key": os.getenv("OPENAI_API_KEY"),
}
]
llm_config = {
"config_list": config_list,
"timeout": 60,
}
# Create a single agent
assistant = autogen.AssistantAgent(
name="assistant",
llm_config=llm_config,
system_message="You are a helpful AI assistant."
)
user_proxy = autogen.UserProxyAgent(
name="user_proxy",
human_input_mode="TERMINATE",
max_consecutive_auto_reply=10,
is_termination_msg=lambda x: x.get("content", "").rstrip().endswith("TERMINATE"),
code_execution_config={"last_n_messages": 3, "work_dir": "coding"},
)
# Initiate a conversation
user_proxy.initiate_chat(
assistant,
message="How can I implement a basic web scraper in Python?"
)
```
## Examples
AG2 Async Agent Chat with Automated Responses
Demonstrates asynchronous human input with AG2 agents.
Example of AG2 agents using a Wikipedia search tool.
Orchestrate a team of specialized agents (researcher, coder, critic) with full AgentOps tracing.
## Resources
Official AG2 documentation on integrating with AgentOps.
Full observability for multi-agent systems with AG2's built-in tracing.