ant-ai 1.0.0

ANT AI

A lightweight Python framework for building tool-driven AI agents and multi-agent systems.

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ANT AI provides a composable set of primitives for building production-ready AI agents: a ReAct reasoning loop, a flexible tool system with MCP support, a graph-based workflow engine, and first-class agent-to-agent (A2A) communication via the A2A protocol.

Features

• ReAct agent — built-in Reason→Act loop with streaming, structured output, and configurable retry logic
• Flexible tools — define tools as decorated functions, class namespaces, or load them directly from any MCP server
• Workflow engine — graph-based orchestration with static and conditional edges to sequence agent behaviour predictably
• Multi-agent colony — wire agents together with the A2A protocol; each agent becomes a callable tool to its peers
• LLM-agnostic — ships with LiteLLM and native OpenAI backends; any ChatLLM-conforming implementation works
• Observability — structured lifecycle events with Langfuse, OpenTelemetry, and log sinks
• Lifecycle hooks — intercept and control every LLM call: pass, block, retry, or substitute results; ships with a GuardrailsAI adapter

Installation

Requires Python 3.14+. Install with uv:

uv add ant-ai

Or clone and sync for local development:

git clone <repo-url>
cd ant-ai
uv sync --all-extras

Quickstart

Single agent

from ant_ai import Agent, Message, State, tool
from ant_ai.llm.integrations import LiteLLMChat

@tool
def get_weather(city: str) -> str:
    """Return the current weather for a city."""
    return f"Sunny, 22°C in {city}"

llm = LiteLLMChat(model="gpt-4o-mini")

agent = Agent(
    name="WeatherAgent",
    system_prompt="You are a helpful weather assistant.",
    llm=llm,
    tools=[get_weather],
)

state = State(messages=[Message(role="user", content="What's the weather in Lugano?")])
answer = agent.invoke(state)
print(answer)

Streaming events

from ant_ai.core import FinalAnswerEvent

async for event in agent.stream(state):
    if isinstance(event, FinalAnswerEvent):
        print(event.content)

Structured output

from pydantic import BaseModel

class WeatherReport(BaseModel):
    city: str
    temperature: int
    condition: str

answer = agent.invoke(state, response_schema=WeatherReport)
# answer is a JSON string matching WeatherReport

Development

# Install dev dependencies and pre-commit hooks
uv sync --all-extras
uv run pre-commit install

# Run tests
uv run pytest

# Serve docs locally
uv run mkdocs serve

See CONTRIBUTING.md for the full contributing guide, branching model, and review process.

License

This software is licensed under the MIT license. See the LICENSE file for details.