nonoka 1.1.1

A production-grade, type-safe Python Agent framework

Nonoka

A production-grade, type-safe Python agent framework with deterministic orchestration, conversational execution, and first-class MCP integration.

Features

• Type-safe core — Pydantic-validated schemas throughout; agents, tools, and plans are all strongly typed
• Deterministic orchestration — Plan + Step + ref() for explicit control flow, not just prompt-and-pray
• Conversational execution — ReActAgent, ReflectiveAgent, and PlanExecutor paradigms out of the box
• First-class tools — @tool decorator with automatic Pydantic schema generation
• Prompt engineering — @prompt decorator and PromptTemplate for composable, type-safe prompt construction
• MCP ready — built-in MCP (Model Context Protocol) support via mcp
• Resilient execution — structured error taxonomy (TransientError, LogicError, SafetyError, etc.) with configurable RetryPolicy
• Observable hooks — Hooks system for tracing, logging, and custom middleware
• Multi-backend LLM — powered by litellm, supporting OpenAI, Anthropic, DeepSeek, and 100+ providers

Installation

pip install nonoka

Or with uv:

uv add nonoka

Quick Start

import asyncio
import nonoka

@nonoka.tool
def get_weather(city: str) -> str:
    """Get the weather for a city."""
    return f"Sunny in {city}!"

async def main():
    agent = nonoka.Agent(
        name="weather-bot",
        tools=[get_weather],
    )
    result = await agent.run("What's the weather in Tokyo?")
    print(result.output)

asyncio.run(main())

Plans & Orchestration

Explicit multi-step workflows with type-safe references:

from nonoka import PlanBuilder, ref

plan = (
    PlanBuilder()
    .add_step("research", "Search for information")
    .add_step("summarize", "Summarize findings", depends_on=[ref("research")])
    .build()
)

executor = nonoka.PlanExecutor(plan=plan)
result = await executor.run("Latest AI breakthroughs")

Prompt Templates

Composable, type-safe prompts:

from nonoka import prompt, PromptTemplate

@prompt
def translate(text: str, target: str = "Chinese") -> str:
    """Translate the following text to {target}:

    {text}
    """

# Or programmatically
tpl = PromptTemplate("Summarize this in {style}:\n{content}")
output = tpl.render(style="bullet points", content=long_text)

ReAct Agent

agent = nonoka.ReActAgent(tools=[search, calculator])
result = await agent.run("What is 42 * the current temperature in Paris?")

Configuration

Nonoka supports three ways to configure agents: declarative files (YAML/JSON/TOML), fluent builders, and direct code.

Declarative Config (YAML)

Write a nonoka.yaml and load it:

# nonoka.yaml
agents:
  weather_assistant:
    model: gpt-4o
    system_prompt: "You are a weather assistant."
    max_turns: 10
    tools:
      - import: my_tools.weather:get_weather

  code_assistant:
    model: deepseek-chat
    system_prompt: "You are a coding assistant."

runner:
  checkpoint: memory
  memory: in_memory

defaults:
  model: deepseek-chat
  max_turns: 10

from nonoka import Config

config = Config.load("nonoka.yaml")           # or Config.auto_find()
agent = config.agents["weather_assistant"].build()
runner = config.runner.build()

Single-agent shorthand (no agents: dict needed):

agent:
  model: gpt-4o
  system_prompt: "You are helpful."

agent = config.agent.build()

Environment Variables in Config

Use ${VAR} or ${VAR:-default} in YAML values:

agent:
  model: ${NONOKA_MODEL:-gpt-4o}
  system_prompt: ${NONOKA_PROMPT}

Fluent Builder API

from nonoka import AgentBuilder, tool

@tool
async def get_weather(city: str) -> str:
    return f"Sunny in {city}!"

agent = (
    AgentBuilder()
    .model("gpt-4o")
    .system_prompt("You are a weather assistant.")
    .tool(get_weather)
    .tool_by_import("my_tools.search:search_city")
    .max_turns(20)
    .retry(max_retries=5, backoff=1.5)
    .metadata(category="weather")
    .tag("production")
    .build()
)

From Dict / YAML / JSON

from nonoka import Agent

# From dict
agent = Agent.from_dict({
    "model": "gpt-4o",
    "tools": ["my_tools:get_weather"],
})

# From file
agent = Agent.from_yaml("agent.yaml")
agent = Agent.from_json("agent.json")

Environment-driven Settings

Nonoka also integrates with pydantic-settings for framework-level config:

from nonoka.core.config import settings

print(settings.default_model)   # from NONOKA_DEFAULT_MODEL env var
print(settings.openai_api_key)  # from NONOKA_OPENAI_API_KEY env var

Requirements

• Python >= 3.10

License

MIT