AutoGen Quickstart

pip install "visiq>=0.5.0" "autogen-agentchat>=0.7,<0.8" "autogen-ext[openai]>=0.7,<0.8"

# pip install visiq autogen-agentchat "autogen-ext[openai]"
import asyncio

from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient
from visiq import govern


def issue_refund(customer_id: str, amount: int) -> str:
    """Issue a refund to a customer."""
    return f"Refunded ${amount} to {customer_id}"


def search_knowledge(query: str) -> list:
    """Search the knowledge base."""
    # Your real RAG store returns the same shape: a list of
    # {"page_content", "metadata"} dicts the retrieval facet can filter.
    return [
        {"page_content": f"Q3 revenue was $4.2M. (matched: {query})",
         "metadata": {"classification": "internal"}},
    ]


agent = AssistantAgent(
    "support",
    model_client=OpenAIChatCompletionClient(model="gpt-4o-mini"),
    tools=[issue_refund, search_knowledge],
    system_message="You are a helpful assistant.",
    reflect_on_tool_use=True,  # let the model read the tool results and answer
)

# ── One call: every tool on this agent is now governed ──
govern(agent, agent_id="support-bot", retrieval_tools={"search_knowledge"})


async def main() -> None:
    result = await agent.run(task="What was Q3 revenue?")
    print(result.messages[-1].content)


asyncio.run(main())

# pip install visiq autogen-agentchat "autogen-ext[openai]"
from autogen_agentchat.agents import AssistantAgent
from autogen_agentchat.conditions import MaxMessageTermination
from autogen_agentchat.teams import RoundRobinGroupChat
from autogen_ext.models.openai import OpenAIChatCompletionClient
from visiq import govern

client = OpenAIChatCompletionClient(model="gpt-4o-mini")
billing = AssistantAgent("billing", model_client=client, tools=[issue_refund])
research = AssistantAgent("research", model_client=client, tools=[search_knowledge])

team = RoundRobinGroupChat([billing, research], termination_condition=MaxMessageTermination(6))
govern(team, agent_id="support-bot", retrieval_tools={"search_knowledge"})

# pip install visiq "autogen-ext[anthropic]" boto3
import os

from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.anthropic import AnthropicBedrockChatCompletionClient, BedrockInfo
from visiq import govern

model_client = AnthropicBedrockChatCompletionClient(
    model="us.anthropic.claude-haiku-4-5-20251001-v1:0",
    bedrock_info=BedrockInfo(
        aws_access_key=os.environ["AWS_ACCESS_KEY_ID"],
        aws_secret_key=os.environ["AWS_SECRET_ACCESS_KEY"],
        # Empty string, not os.environ[...]: a long-lived IAM key has no session
        # token, and the field is required, so indexing it raises KeyError.
        aws_session_token=os.environ.get("AWS_SESSION_TOKEN", ""),
        aws_region="us-east-1",
    ),
    # Required: AutoGen cannot infer capabilities from a Bedrock model id.
    model_info={"vision": False, "function_calling": True, "json_output": False,
                "family": "claude-haiku-4-5", "structured_output": False},
)

agent = AssistantAgent("support", model_client=model_client,
                       tools=[issue_refund, search_knowledge], reflect_on_tool_use=True)
govern(agent, agent_id="support-bot", retrieval_tools={"search_knowledge"})