ant-ai 1.0.4

A lightweight Python framework for building multi-agent AI systems

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

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ant-ai is a lightweight Python framework for building multi-agent systems: graph-based workflow orchestration, first-class agent-to-agent (A2A) communication via the A2A protocol, MCP tool integration, lifecycle hooks for guardrails, and built-in observability — all on top of an LLM-agnostic core.

Why ANT AI

Multi-agent by design. Agents communicate and delegate natively via the A2A protocol — build systems that grow without rewrites.

No lock-in. Swap LLMs, tools, or observability backends in one line. Your logic stays untouched.

Structured, not scripted. Model complex behavior as graphs. Know exactly what runs, when, and why.

Production-ready out of the box. Hooks, guardrails, and full observability via Langfuse and OpenTelemetry — without extra setup.

Installation

Requires Python 3.14+. Install with uv:

uv add ant-ai

Or clone and sync for local development:

git clone https://github.com/idea-idsia/ant-ai
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.