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AIMU AIMU

AIMU

AI Modeling Utilities — a lightweight Python library for building AI-powered applications with a consistent, provider-agnostic interface across text, images, and audio.

Language models are the primary building block, with the same interface extending to image generation and audio processing. AIMU separates autonomous agents from code-controlled workflows, and treats agents as composable units that can be used anywhere a plain model client is accepted. Tool integration is structural (not a plugin), semantic and document memory can be dropped in, and a prompt-tuning loop optimises prompts against labelled data without ML machinery.

Install

pip install aimu[all]

Or pick the providers you need: aimu[ollama], aimu[anthropic], aimu[openai_compat], aimu[hf] (text + HF diffusers image generation), aimu[google] (Google Nano Banana image generation), aimu[llamacpp].

Quick start

import aimu

# One-shot
text = aimu.chat("Hello", model="anthropic:claude-sonnet-4-6")

# Multi-turn
client = aimu.client("ollama:qwen3.5:9b", system="You are concise.")
client.chat("Hi there")
client.chat("What did I just say?")     # history preserved

That's the full mental model: a chat() function for one-shots, a client() factory for conversations, and provider:model_id strings to swap backends.

Where to next

  • Tutorials

    Hands-on walkthroughs. Start here if you're new — install to first working agent in 15 minutes.

  • How-to guides

    Task-oriented recipes. "How do I swap providers / write a tool / stream output / benchmark models?"

  • Reference

    The full API surface, capability matrices, environment variables, and CLI commands.

  • Explanation

    The why. Architecture, design principles, the agent/workflow taxonomy, and what AIMU deliberately doesn't do.

What's in the box

  • Provider-agnostic clients — Ollama, HuggingFace, llama-cpp, Anthropic, OpenAI, Gemini, plus every OpenAI-compatible local server (LM Studio, vLLM, SGLang, llama-server, HF Transformers Serve).
  • Text-to-image — aimu.image_client() and aimu.generate_image() parallel the text surface. HuggingFace diffusers for local generation (SD 1.5 / SDXL / SD 3.5 / FLUX), Google Nano Banana for cloud. Drops into any chat agent via the built-in generate_image tool. See how-to: generate images.
  • Agents and workflows — Agent for autonomous tool-using loops; Chain / Router / Parallel / EvaluatorOptimizer for code-controlled patterns from Anthropic's Building Effective Agents.
  • Tools — @tool decorator for plain Python functions, plus a synchronous MCPClient wrapper for cross-process tools.
  • Skills — filesystem-discovered SKILL.md files that auto-inject capabilities into a SkillAgent.
  • Memory — semantic facts (ChromaDB), path-based documents (Anthropic Memory API), and conversation history (TinyDB).
  • Prompt management — versioned SQLite catalog plus a hill-climbing tuner with classification, multi-class, extraction, and judged variants.
  • Evaluation — DeepEval integration and a multi-model benchmark harness with CSV / JSON / catalog export.
  • Optional async surface — aimu.aio mirrors the whole sync API (same class names, one-import-away). Parallel and concurrent_tool_calls use asyncio.TaskGroup for structured concurrency. See async design.

Notebooks

The notebooks/ directory ships 15 runnable demos covering every subsystem end-to-end, including a dedicated 14 - Async notebook for the aimu.aio surface and 15 - Image Generation for text-to-image. The README lists each one.