The ConversableAgent is the fundamental building block of AG2 - think of it as both the brain and the personality of your AI system. With an LLM configuration providing its thinking power, a ConversableAgent can:

  • Communicate with other agents and humans
  • Process information and generate responses
  • Follow instructions defined in its system message
  • Execute tools and functions when needed

Every agent in your AG2 system is either a ConversableAgent or built upon one, making it the most important class to understand.

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Creating a ConversableAgent

Once you’ve set up your LLM configuration, creating a ConversableAgent is straightforward:

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from autogen import ConversableAgent, LLMConfig

# Create LLM configuration first
llm_config = LLMConfig(api_type="openai", model="gpt-4o-mini")

# Create the agent using the context manager approach
my_agent = ConversableAgent(
    name="helpful_agent",  # Give your agent a unique name
    system_message="You are a helpful AI assistant",  # Define its personality and purpose
    llm_config=llm_config  # Pass the LLM configuration
)

Key Parameters

When creating a ConversableAgent, there are several important parameters to consider:

  • name: A unique identifier for your agent
  • system_message: Instructions that define the agent’s role, personality, and behavior
  • llm_config: Configuration for the language model (can be passed directly or via context manager)

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Interacting with a ConversableAgent

The simplest way to interact with a ConversableAgent is to use the run() and process() methods. Here’s a basic example:

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# Establish the workflow
response = my_agent.run(
    message="What's the capital of France?",
    max_turns=2,  # Limit conversation length
    user_input=True  # Allow user to provide input
)

# Process the workflow
response.process()

???+ info

Why Two Steps: run() and process()

You might wonder why we need to call both run() and process() to get results.

👉 Here’s what’s happening:

When you call run(), it doesn’t immediately give you the final output. Instead, it returns an iterator, a special object that holds a stream of events, messages, and metadata.

👉 Why? Because flexibility matters.

The workflow steps won’t actually start running until you iterate over this iterator. This design gives you full control and makes it easy to build things like:

  • Custom UIs
  • Real-time dashboards
  • Interactive apps where you control how and when each event is handled

👉 Okay — so what does process() do?

process() is a built-in helper method that takes care of iterating through those events for you. It simulates a chat-like console experience — printing messages, handling user inputs, and making it feel like a live conversation.

👉 In short:

Use run() and iterate over the events yourself when you want full control over the workflow’s events and how they are processed. 👉 Learn more about the run() method here →

Use process() along with run() when you just want a quick, ready-to-go chat experience in the console.

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Financial Compliance Example

Let’s create a simple financial agent using ConversableAgent:

from autogen import ConversableAgent, LLMConfig
import os

# Configure the LLM (we created this in the previous section)
llm_config = LLMConfig(
    api_type="openai",
    model="gpt-4o-mini",
    api_key=os.environ["OPENAI_API_KEY"],
    temperature=0.2
)

# Create a basic financial agent
with llm_config:
    finance_agent = ConversableAgent(
        name="finance_agent",
        system_message="You are a financial assistant who helps analyze financial data and transactions."
    )

# Run the agent with a prompt
response = finance_agent.run(
    message="Can you explain what makes a transaction suspicious?",
    max_turns=1
)

# Iterate through the chat automatically with console output
response.process()

In this simple example:

  • We’ve created a financial agent with a basic system message
  • We’re asking it a straightforward question about suspicious transactions
  • We’re limiting the conversation to just one turn by setting max_turns=1. This is one of the many ways to end a conversation. You can find more options in the Ending a chat.

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How to Run This Example

  • Save the code above to a file (e.g., financial_compliance.py)
  • Set your OpenAI API key in your environment variable or use your preferred model provider
  • Make sure you have AG2 installed: pip install ag2[openai]
  • Run the script: python financial_compliance.py

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Example Output

When you run the code, you should see output similar to this:

user (to finance_agent):

Can you explain what makes a transaction suspicious?

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>>>>>>>> USING AUTO REPLY...
finance_agent (to user):

Certainly! A transaction may be considered suspicious for several reasons, often related to patterns or behaviors that deviate from the norm. Here are some common indicators:

1. **Unusual Amounts**: Transactions that are significantly larger or smaller than typical amounts for a particular account or customer can raise red flags.

2. **Inconsistent Behavior**: If a customer's transaction history suddenly changes—such as a sudden increase in frequency or volume of transactions—it may warrant further investigation.

3. **Geographic Anomalies**: Transactions involving countries or regions known for high levels of fraud or money laundering can be suspicious, especially if the customer has no known connections to those areas.

4. **Rapid Movement of Funds**: Transactions that involve moving money quickly between accounts, especially if they are done in a short time frame, can indicate attempts to obscure the source of funds.

5. **Structuring or Smurfing**: Breaking up large transactions into smaller ones to avoid detection or reporting thresholds can be a sign of suspicious activity.

6. **Lack of Clear Purpose**: Transactions that lack a clear business or personal purpose, or that do not align with the customer's known activities, can be suspicious.

7. **High-Risk Industries**: Transactions involving businesses in industries that are considered high-risk for fraud, such as gambling, adult entertainment, or cryptocurrency, may be flagged.

8. **Unusual Account Activity**: Sudden changes in account behavior, such as a dormant account suddenly becoming active or frequent withdrawals without corresponding deposits, can be suspicious.

9. **Third-Party Transactions**: Transactions involving third parties who are not directly related to the account holder can raise concerns, especially if they are frequent or involve large sums.

10. **Negative Media Reports**: If a person or entity is associated with negative news reports related to fraud, money laundering, or other illicit activities, their transactions may be scrutinized.

Financial institutions often use automated systems to monitor transactions for these and other indicators, and they may file Suspicious Activity Reports (SARs) when they identify potentially suspicious transactions.

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>>>>>>>> TERMINATING RUN (16d4d75c-ca0d-4952-bfff-f7802a57e5e7): Maximum turns (1) reached

This is just the beginning of what you can do with ConversableAgent. In subsequent sections, we’ll expand this example to handle more complex financial tasks and interactions.

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Next Steps

Now that you understand the basics of ConversableAgent, it’s time to add some human oversight!

For financial compliance systems, a human review of flagged transactions adds an essential layer of security and compliance. In the next section, you’ll see how to build upon our simple example to create a more robust financial assistant with human oversight.

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