Metadata-Version: 2.4
Name: atomicguard
Version: 1.1.0
Summary: A Dual-State Agent Framework for reliable LLM code generation with guard-validated loops
Author-email: Matthew Thompson <thompsonson@gmail.com>
Maintainer-email: Matthew Thompson <thompsonson@gmail.com>
License: MIT
Project-URL: Homepage, https://github.com/thompsonson/atomicguard
Project-URL: Repository, https://github.com/thompsonson/atomicguard
Project-URL: Documentation, https://github.com/thompsonson/atomicguard#readme
Project-URL: Issues, https://github.com/thompsonson/atomicguard/issues
Project-URL: Changelog, https://github.com/thompsonson/atomicguard/blob/main/CHANGELOG.md
Keywords: llm,agents,code-generation,neuro-symbolic,guards,ai,validation
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Programming Language :: Python :: 3.14
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development :: Code Generators
Classifier: Typing :: Typed
Requires-Python: >=3.12
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: matplotlib>=3.10.0
Requires-Dist: openhands-ai>=0.27.0
Requires-Dist: pydantic-ai>=1.0.0
Requires-Dist: pytestarch>=4.0.1
Dynamic: license-file

# AtomicGuard

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A Dual-State Agent Framework for reliable LLM code generation.

> **New to AtomicGuard?** Start with the [Getting Started Guide](docs/getting-started.md).

**Paper:** *Managing the Stochastic: Foundations of Learning in Neuro-Symbolic Systems for Software Engineering* (Thompson, 2025)

## Overview

AtomicGuard implements guard-validated generation loops that dramatically improve LLM reliability. The core abstraction is the **Atomic Action Pair** ⟨agen, G⟩ — coupling each generation action with a validation guard.

Key results (Yi-Coder 9B, n=50):

| Task | Baseline | Guarded | Improvement |
|------|----------|---------|-------------|
| Template | 35% | 90% | +55pp |
| Password | 82% | 98% | +16pp |
| LRU Cache | 94% | 100% | +6pp |

## Installation

```bash
# From PyPI
pip install atomicguard

# From source
git clone https://github.com/thompsonson/atomicguard.git
cd atomicguard
uv venv && source .venv/bin/activate
uv pip install -e ".[dev,test]"
```

## Quick Start

```python
from atomicguard import (
    OllamaGenerator, SyntaxGuard, TestGuard,
    CompositeGuard, ActionPair, DualStateAgent,
    InMemoryArtifactDAG
)

# Setup
generator = OllamaGenerator(model="qwen2.5-coder:7b")
guard = CompositeGuard([SyntaxGuard(), TestGuard("assert add(2, 3) == 5")])
action_pair = ActionPair(generator=generator, guard=guard)
agent = DualStateAgent(action_pair, InMemoryArtifactDAG(), rmax=3)

# Execute
artifact = agent.execute("Write a function that adds two numbers")
print(artifact.content)
```

See [examples/](examples/) for more detailed usage, including a [mock example](examples/basic_mock.py) that works without an LLM.

## Benchmarks

Run the simulation from the paper:

```bash
python -m benchmarks.simulation --model yi-coder:9b --trials 50 --task all --output results/results.db --format sqlite

# Generate report
python -m benchmarks.simulation --visualize --output results/results.db --format sqlite
```

## Project Structure

```
atomicguard/
├── src/atomicguard/     # Core library
├── benchmarks/          # Simulation code
├── docs/design/         # Design documents
├── examples/            # Usage examples
└── results/             # Generated reports & charts
```

## Citation

If you use this framework in your research, please cite the paper:

> Thompson, M. (2025). Managing the Stochastic: Foundations of Learning in Neuro-Symbolic Systems for Software Engineering. arXiv preprint arXiv:2512.20660.

```bibtex
@article{thompson2025managing,
  title={Managing the Stochastic: Foundations of Learning in Neuro-Symbolic Systems for Software Engineering},
  author={Thompson, Matthew},
  journal={arXiv preprint arXiv:2512.20660},
  year={2025},
  url={[https://arxiv.org/abs/2512.20660](https://arxiv.org/abs/2512.20660)}
}

``` 

## License

MIT
