PyFuncAI 0.1.5

Prompt-defined Python functions with lazy or eager LLM-backed generation.

PyFuncAI

PyFuncAI is a Python library for defining functions with prompts and generating their implementations with an LLM at runtime.

The library is built around a small API:

• connect to a model provider
• describe a function in natural language
• request lazy or eager generation
• optionally cache generated source for reuse across runs

PyFuncAI currently supports local Ollama models and remote OpenAI and Gemini providers.

Installation

Install from PyPI:

pip install pyfuncai

For local development:

pip install -e .[dev]

Quick Start

Ollama

from pyfuncai import connect, create_function

connect(
    "ollama",
    model="qwen3.5:latest",
    base_url="http://localhost:11434",
    timeout=180,
)

greet = create_function(
    "Return a short greeting for the provided name.",
    signature="(name: str) -> str",
    function_name="greet",
    mode="eager",
    cache=True,
)

print(greet("Alice"))

OpenAI

from pyfuncai import connect, create_function

connect(
    "openai",
    model="gpt-5-mini",
    api_key="YOUR_OPENAI_API_KEY",
)

slugify = create_function(
    "Convert text into a URL-friendly slug.",
    signature="(text: str) -> str",
    function_name="slugify",
)

Gemini

from pyfuncai import connect, create_function

connect(
    "gemini",
    model="gemini-2.5-flash",
    api_key="YOUR_GEMINI_API_KEY",
)

summarize = create_function(
    "Summarize a short paragraph in one sentence.",
    signature="(text: str) -> str",
    function_name="summarize",
)

Core Concepts

connect(provider, **config)

Registers the default provider used by subsequent create_function() calls.

Supported providers:

• ollama
• openai
• gemini

Common configuration keys:

• model
• timeout
• cache_dir

Provider-specific keys:

• Ollama: base_url
• OpenAI: api_key, base_url
• Gemini: api_key, base_url

create_function(...)

Creates a prompt-defined callable.

Common arguments:

• prompt
• signature
• function_name
• mode="lazy" or mode="eager"
• cache=True
• provider
• cache_dir
• system_prompt
• temperature
• max_output_tokens
• allow_modules

The return value is a GeneratedFunction. It behaves like a normal callable and also exposes:

• .build(force=False)
• .source
• .cache_key
• .is_built

createFunction(...) is available as a compatibility alias.

Generation Modes

• lazy: generation happens on first invocation
• eager: generation happens during create_function()

Caching

Generated source can be cached on disk and reused across runs. Cache keys include:

• prompt
• signature
• function name
• provider identity
• mode
• allowed modules

The default cache location is OS-specific. You can override it globally with connect(..., cache_dir=...) or per function with create_function(..., cache_dir=...).

CLI

PyFuncAI ships with a small command-line interface for prebuilding functions and inspecting cached source.

Prebuild every prompt-defined function created by a script:

pyfuncai build path/to/script.py

Force regeneration even when cached source already exists:

pyfuncai build --force path/to/script.py

Inspect the cache directory:

pyfuncai cache path
pyfuncai cache list
pyfuncai cache clear

You can point cache commands at a different directory with --cache-dir.

Safety

PyFuncAI validates generated code before execution. The current implementation includes:

• AST validation
• exact signature matching
• restricted imports
• restricted builtins at execution time

This is still a code-generation library, not a hardened sandbox. Generated code should be treated as untrusted.

Development

The repository uses three long-lived branches:

• main: stable development and PyPI releases
• preview: prerelease validation and release-candidate testing
• dev: active integration branch

Automation lives under .github/workflows/:

• ci.yml: formatting, tests, and package build checks on main, preview, dev, and pull requests
• release.yml: stable releases from main tags such as v0.1.3
• prerelease.yml: GitHub prereleases from preview tags such as v0.1.4rc1

Run the test suite locally:

pytest

Run the optional live Ollama test:

PYFUNCAI_RUN_OLLAMA_TESTS=1 pytest tests/test_ollama_live.py

PowerShell:

$env:PYFUNCAI_RUN_OLLAMA_TESTS = "1"
pytest tests/test_ollama_live.py

Releasing

Stable releases are published to PyPI from main.

Typical stable release flow:

1. Update version in pyproject.toml.
2. Commit and push to main.
3. Create and push a matching tag.

Example:

git checkout main
git pull
git tag v0.1.3
git push origin main
git push origin v0.1.3

Preview releases are created from preview and published as GitHub prereleases only.

Example:

git checkout preview
git pull
git tag v0.1.4rc1
git push origin preview
git push origin v0.1.4rc1

Contributing

Contributions are welcome. A good contribution usually includes:

• a clear problem statement or use case
• tests for behavior changes
• documentation updates when the public API changes

Before opening a pull request:

python -m black src tests example.py
python -m pytest
python -m build

License

Apache-2.0