interaxions 0.0.1.post1

A library for agent and environment protocol interactions

Interaxions

A modern, extensible framework for orchestrating AI agents and environments on Kubernetes/Argo Workflows, inspired by HuggingFace Transformers.

✨ Features

• 🎯 Job-Based Configuration - Unified Job schema for complete workflow definition
• 🚀 Dynamic Loading - Load components from built-in, local, or remote Git repositories
• 🔄 Unified API - All Auto* classes use consistent from_repo() interface
• 📦 Three-Layer Architecture - Scaffolds, Environments, and Workflows
• 🏷️ Version Control - Support for Git tags, branches, and commits
• 🔒 Multi-Process Safe - File locks for concurrent access
• 💾 Smart Caching - Three-level cache system for optimal performance
• 🌐 Flexible Sources - GitHub, GitLab, HuggingFace, OSS, or custom sources
• ✅ Comprehensive Testing - 53 unit tests with pytest

🚀 Quick Start

Installation

# Basic installation
pip install interaxions

# With optional dependencies
pip install interaxions[argo]  # Argo Workflows support
pip install interaxions[hf]    # HuggingFace datasets
pip install interaxions[oss]   # OSS storage support

# For development
pip install -e ".[dev]"

Basic Usage (Job-Based API)

from interaxions import AutoWorkflow
from interaxions.schemas import Job, Scaffold, Environment, Workflow, Runtime, LiteLLMModel

# Define a complete job configuration
job = Job(
    name="fix-django-bug",
    description="Fix Django bug using SWE-agent",
    tags=["swe-bench", "django"],
    labels={"priority": "high", "team": "research"},
    
    # Model configuration
    model=LiteLLMModel(
        type="litellm",
        provider="openai",
        model="gpt-4",
        base_url="https://api.openai.com/v1",
        api_key="your-api-key",
    ),
    
    # Scaffold (agent) configuration
    scaffold=Scaffold(
        repo_name_or_path="swe-agent",
        params={"max_iterations": 10},
    ),
    
    # Environment configuration
    environment=Environment(
        repo_name_or_path="swe-bench",
        environment_id="django__django-12345",
        source="hf",
        params={
            "dataset": "princeton-nlp/SWE-bench",
            "split": "test",
        },
    ),
    
    # Workflow configuration
    workflow=Workflow(
        repo_name_or_path="rollout-and-verify",
        params={},
    ),
    
    # Runtime configuration
    runtime=Runtime(
        namespace="experiments",
        service_account="argo-workflow",
        ttl_seconds_after_finished=3600,
    ),
)

# Create and submit workflow
workflow_template = AutoWorkflow.from_repo(job.workflow.repo_name_or_path)
workflow = workflow_template.create_workflow(job)
workflow.create()  # Submit to Argo

Quick API (One-Step Loading)

from interaxions import AutoScaffold, AutoEnvironment, AutoWorkflow

# Load scaffold
scaffold = AutoScaffold.from_repo("swe-agent")

# Load environment (unified API)
env = AutoEnvironment.from_repo(
    repo_name_or_path="swe-bench",
    environment_id="django__django-12345",
    source="hf",
    dataset="princeton-nlp/SWE-bench",
    split="test",
)

# Load workflow
workflow_template = AutoWorkflow.from_repo("rollout-and-verify")

📚 Core Concepts

1. Job - Unified Configuration

Job is the central schema that encapsulates all information needed to run a workflow:

from interaxions.schemas import Job

job = Job(
    # Metadata
    name="my-job",
    description="Job description",
    tags=["tag1", "tag2"],
    labels={"key": "value"},
    
    # Components (all use from_repo pattern)
    model=...,        # LLM configuration
    scaffold=...,     # Agent/scaffold configuration
    environment=...,  # Environment/data configuration  
    workflow=...,     # Workflow orchestration
    runtime=...,      # Kubernetes/Argo settings
)

2. Three-Layer Architecture

Scaffolds (formerly Agents)

• High-level orchestration logic
• Can manage single or multiple agents internally
• Example: swe-agent

Environments

• Test environments and evaluation datasets
• Support HuggingFace, OSS, and custom sources
• Example: swe-bench

Workflows

• Define execution order and dependencies
• Generate Argo Workflows
• Example: rollout-and-verify

3. Dynamic Loading

All components use the from_repo() pattern:

# Built-in
component = Auto*.from_repo("component-name")

# Local path
component = Auto*.from_repo("./my-component")

# Remote repository (GitHub)
component = Auto*.from_repo("username/repo-name")

# With specific version
component = Auto*.from_repo("username/repo", revision="v1.0.0")

🎨 Loading Sources

Built-in Components

from interaxions import AutoScaffold, AutoEnvironment, AutoWorkflow

# Load built-in components
scaffold = AutoScaffold.from_repo("swe-agent")
workflow = AutoWorkflow.from_repo("rollout-and-verify")

Environment Loading (Unified API)

from interaxions import AutoEnvironment

# From HuggingFace
env = AutoEnvironment.from_repo(
    repo_name_or_path="swe-bench",
    environment_id="django-123",
    source="hf",
    dataset="princeton-nlp/SWE-bench",
    split="test",
)

# From OSS
env = AutoEnvironment.from_repo(
    repo_name_or_path="swe-bench",
    environment_id="django-123",
    source="oss",
    dataset="swe-bench-data",
    split="test",
    oss_region="cn-hangzhou",
    oss_endpoint="oss-cn-hangzhou.aliyuncs.com",
    oss_access_key_id="your-key-id",
    oss_access_key_secret="your-secret",
)

Batch Loading (Factory Pattern)

from interaxions import AutoEnvironmentFactory

# Load factory once
factory = AutoEnvironmentFactory.from_repo("swe-bench")

# Create multiple environments efficiently
env1 = factory.get_from_hf("django-123", "dataset", "test")
env2 = factory.get_from_hf("flask-456", "dataset", "test")
env3 = factory.get_from_hf("numpy-789", "dataset", "test")

🔧 Environment Variables (Optional)

All environment variables have sensible defaults and are optional:

Variable	Description	Default
IX_HOME	Base directory for Interaxions data	~/.interaxions
IX_HUB_CACHE	Cache directory for hub modules	~/.interaxions/hub
IX_OFFLINE	Enable offline mode (no network)	false
IX_ENDPOINT	Custom Git endpoint for remote repos	GitHub

Example:

export IX_HOME=/custom/path
export IX_OFFLINE=true

📦 Creating Custom Components

See Repository Standards for detailed requirements.

Minimum Requirements

Scaffold Repository:

my-scaffold/
├── config.yaml           # type: my-scaffold
├── agent.py              # Class inheriting from BaseScaffold
└── templates/            # Optional Jinja2 templates
    └── main.j2

Environment Repository:

my-environment/
├── config.yaml           # type: my-environment
└── env.py                # Factory inheriting from BaseEnvironmentFactory

Workflow Repository:

my-workflow/
├── config.yaml           # type: my-workflow
└── workflow.py           # Class inheriting from BaseWorkflow

All components must implement:

• from_repo(repo_name_or_path, revision) class method
• create_task(job, **kwargs) or create_workflow(job, **kwargs) method

🧪 Testing

# Run unit tests (fast, reliable)
pytest -m unit

# Run all tests
pytest

# With coverage
pytest --cov=interaxions --cov-report=html

# View coverage report
open htmlcov/index.html

See tests/README.md for detailed testing documentation.

📁 Project Structure

interaxions/
├── scaffolds/          # Agent scaffold implementations
│   ├── base_scaffold.py
│   └── swe_agent/
├── environments/       # Environment implementations
│   ├── base_environment.py
│   └── swe_bench/
├── workflows/          # Workflow implementations
│   ├── base_workflow.py
│   └── rollout_and_verify/
├── schemas/            # Pydantic schemas (Job, Scaffold, etc.)
│   ├── job.py
│   └── models.py
└── hub/                # Dynamic loading system
    ├── auto.py         # Auto* classes
    ├── hub_manager.py  # Repository management
    └── constants.py    # Configuration

tests/                  # Comprehensive test suite
├── unit/               # Unit tests (53 tests, all passing)
├── integration/        # Integration tests
├── e2e/                # End-to-end tests
└── conftest.py         # Shared fixtures

examples/               # Usage examples
└── quickstart.py       # Complete tutorial

🔄 Development Workflow

# Clone repository
git clone https://github.com/Hambaobao/interaxions.git
cd interaxions

# Install with dev dependencies
pip install -e ".[dev]"

# Run tests
pytest -m unit

# Run examples
python examples/quickstart.py

# Build package
python -m build

# Check package
twine check dist/*

📖 Documentation

• Repository Standards - Complete guide for creating custom components
• Testing Guide - Comprehensive testing documentation
• Examples - Example implementations and tutorials

🤝 Contributing

Contributions are welcome! Please:

1. Fork the repository
2. Create a feature branch
3. Add tests for new functionality
4. Ensure all tests pass: pytest -m unit
5. Submit a pull request

📄 License

MIT License - see LICENSE for details

🙏 Acknowledgments

• Inspired by HuggingFace Transformers
• Built on Hera for Argo Workflows
• Powered by Pydantic for data validation

🔗 Links

• Homepage: https://github.com/Hambaobao/interaxions
• Issues: https://github.com/Hambaobao/interaxions/issues
• PyPI: Coming soon

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Made with ❤️ for the AI agent research community