nanitics 0.1.1

Python SDK for building single-agent and multi-agent AI systems

Nanitics

Python SDK for building single-agent and multi-agent AI systems.

Why Nanitics?

Nanitics differs from other agent frameworks in three ways:

• Composable primitives, not a framework. Pick the pieces you need — agent strategies, coordination patterns, memory, evaluation, HITL, tools — and compose them. No runtime or opinionated workflow shape is imposed.
• Trace-first observability. Every agent loop, tool call, and coordination event emits a structured event. The built-in Observatory trace viewer turns that into a live debugging surface from day one.
• Real-services validation. Every public component is validated against real LLM providers before release, not just mocks. Mocks drive fast tests; real services prove correctness.

Features

Agent Strategies — Built-in strategies for different problem types: ReAct, Reasoning, Reflexion, ReWOO, CodeAct, LATS, and Tree of Thought.

Memory — Working, episodic, long-term, and semantic memory for persistent agent state.

Orchestration — Compose agents into pipelines, DAGs, loops, conditionals, and map-reduce workflows.

Multi-Agent Coordination — Handoff, supervisor, blackboard, debate, consensus, bidding, broadcast, message bus, peer network, orchestrator, and agent-as-tool patterns.

Evaluation — Programmatic and LLM-based evaluators for assessing agent output quality.

Human-in-the-Loop — Approval gates, revision gates, and durable HITL with checkpoint suspension for long-running workflows.

Tools — Function tools, conditional tools, and tool composition with automatic schema generation.

Observability — Event-based tracing with the Observatory trace viewer for inspecting agent execution.

Planning — Upfront and adaptive planning with goal tracking and plan adherence evaluation.

Safety — Iteration limits, cancellation tokens, and sandboxed code execution.

Quick Start

For a full end-to-end run against a real LLM, see the deployment guide.

Install Nanitics:

pip install nanitics

Create a ReAct agent with a tool, driven by a scripted MockLLMClient so the snippet runs without an API key:

import asyncio
from nanitics import (
    InMemoryEmitter,
    LLMResponse,
    MockLLMClient,
    ReActAgent,
    ToolCall,
    Usage,
    tool,
)

@tool("greet", "Greet someone by name")
async def greet(name: str) -> str:
    return f"Hello, {name}!"

async def main():
    llm = MockLLMClient(responses=[
        LLMResponse(
            content="I'll greet them.",
            tool_calls=[ToolCall(id="1", name="greet", arguments={"name": "world"})],
            usage=Usage(input_tokens=50, output_tokens=20),
            model="mock",
            stop_reason="tool_use",
        ),
        LLMResponse(
            content="Hello, world!",
            tool_calls=[],
            usage=Usage(input_tokens=80, output_tokens=15),
            model="mock",
            stop_reason="end_turn",
        ),
    ])
    agent = ReActAgent(
        name="my-agent",
        llm_client=llm,
        emitter=InMemoryEmitter("trace-001"),
        system_prompt="You are a helpful assistant.",
        tools=[greet],
    )
    result = await agent.run("Say hello to the world")
    print(result.output)

asyncio.run(main())

To run the same agent against a real provider, set ANTHROPIC_API_KEY and swap MockLLMClient(...) for AnthropicLLMClient(model="claude-haiku-4-5"). Everything else above is unchanged.

See the Getting Started guide for a full walkthrough. For the full API, read the docstrings in the source tree under nanitics/.

LLM Providers

Nanitics supports multiple LLM providers:

Provider	Install	Client
Anthropic	pip install nanitics	AnthropicLLMClient
OpenAI	pip install nanitics	OpenAILLMClient
Mistral	pip install nanitics[mistral]	MistralLLMClient
LiteLLM	pip install nanitics[litellm]	LiteLLMClient

For testing and development, use MockLLMClient — no API keys required.

Examples

The examples directory contains runnable examples covering every SDK component. All examples use MockLLMClient for deterministic, API-key-free execution.

See the examples README for a complete index.

Documentation

Primary entry points:

Guide	Description
Getting Started	Build your first agent
Core Concepts	The agent loop, tools, prompts, LLM clients
Agent Types	Agent strategies and when to use each
Multi-Agent Coordination	Coordination patterns for multi-agent systems
Deployment	Full-stack compose, take-to-own-infra, resource and shutdown patterns
API Reference	Generated from source docstrings — signatures, fields, constraints

For the complete catalogue — Memory, Orchestration, Evaluation, HITL, Tools, Planning, Context Management, Error Handling, Safety, Security, Observability, Building Applications, Architecture, SDK Internals, Diagnosing Agent Issues, Testing, Streaming, Production, Built-in Tools, Local LLMs — see the full guides index.

Project

• Status: Pre-1.0. The public API is nanitics.__all__; see the deprecation policy for the change contract.
• Contributing: CONTRIBUTING.md for guidelines, DEVELOPMENT.md for setup.
• Governance: GOVERNANCE.md covers decisions and release cadence; MAINTAINERS.md names the current maintainers.
• Trademark: TRADEMARK.md for who owns the Nanitics name and what uses are permitted.
• Questions and ideas: GitHub Discussions; Getting Help for the channel split.
• Security: report vulnerabilities via SECURITY.md; SDK security posture in the security guide.

License

Apache License 2.0. See LICENSE for the full text.