atomicguard 2.4.0
A Dual-State Agent Framework for reliable LLM code generation with guard-validated loops
License: MIT
Requires Python: >=3.12
Keywords: llm, agents, code-generation, neuro-symbolic, guards, ai, validation

# AtomicGuard

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

## Why AtomicGuard?

AI agents hallucinate. Worse, those hallucinations **compound** — each generation builds on the last, and errors propagate through the workflow.

AtomicGuard solves this by combining to aspects - **decompose goals** into small measurable tasks and through **Bounded Indeterminacy**: the LLM generates content, but a deterministic state machine controls the logic. Every generation is validated before the workflow advances.

| Challenge | Solution |
|-----------|----------|
| 🛡️ **Safety** | Dual-State Architecture & Atomic Action Pairs |
| 💾 **State** | Versioned Repository Items & Configuration Snapshots |
| 🌐 **Scale** | Multi-Agent Coordination via Shared DAG |
| 📈 **Improvement** | Continuous Learning from Guard Verdicts |

→ [Learn more about the architecture](docs/design/architecture.md)

> **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.

## LLM Backends

AtomicGuard supports multiple LLM backends. Each generator implements `GeneratorInterface` and can be swapped in with no other code changes.

### Ollama (local or cloud)

Uses the OpenAI-compatible API. Works with any Ollama-served model:

```python
from atomicguard.infrastructure.llm import OllamaGenerator

# Local instance (default: http://localhost:11434/v1)
generator = OllamaGenerator(model="qwen2.5-coder:7b")
```

### HuggingFace Inference API

Connects to HuggingFace Inference Providers via `huggingface_hub`. Supports any model available through the HF Inference API, including third-party providers like Together AI.

```bash
# Install the optional dependency
pip install huggingface_hub

# Set your API token
export HF_TOKEN="hf_your_token_here"
```

```python
from atomicguard.infrastructure.llm import HuggingFaceGenerator
from atomicguard.infrastructure.llm.huggingface import HuggingFaceGeneratorConfig

# Default: Qwen/Qwen2.5-Coder-32B-Instruct
generator = HuggingFaceGenerator()

# Custom model and provider
generator = HuggingFaceGenerator(HuggingFaceGeneratorConfig(
    model="Qwen/Qwen2.5-Coder-32B-Instruct",
    provider="together",       # or "auto", "hf-inference"
    temperature=0.7,
    max_tokens=4096,
))
```

Drop-in replacement in any workflow:

```python
from atomicguard import (
    SyntaxGuard, TestGuard, CompositeGuard,
    ActionPair, DualStateAgent, InMemoryArtifactDAG
)
from atomicguard.infrastructure.llm import HuggingFaceGenerator

generator = HuggingFaceGenerator()
guard = CompositeGuard([SyntaxGuard(), TestGuard("assert add(2, 3) == 5")])
action_pair = ActionPair(generator=generator, guard=guard)
agent = DualStateAgent(action_pair, InMemoryArtifactDAG(), rmax=3)

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

## 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
@misc{thompson2025managing,
  title={Managing the Stochastic: Foundations of Learning in Neuro-Symbolic Systems for Software Engineering},
  author={Thompson, Matthew},
  year={2025},
  eprint={2512.20660},
  archivePrefix={arXiv},
  primaryClass={cs.LG},
  url={https://arxiv.org/abs/2512.20660}
}
```

## License

MIT